The researchers’ robotic finger contains conductive fiber coils and a twisted liquid-metal fiber at the fingertip. | Source: Hongbo Wang

Researchers at the University of Science and Technology of China said they have developed a soft robotic “finger” with a sense of touch that can perform routine doctor office examinations, including taking a patient’s pulse and checking for abnormal lumps.

The scientists said this technology could make it easier for doctors to detect diseases like breast cancer early, when they are more treatable. It could also put patients at ease during physical exams that can seem uncomfortable and invasive.

“By further development to improve its efficiency, we also believe that a dexterous hand made of such fingers can act as a ‘Robodoctor’ in a future hospital, like a physician,” stated Hongbo Wang, a sensing technologies researcher at the University of Science and Technology of China and an author of the study.

“Combined with machine learning, automatic robotic examination and diagnosis can be achieved, particularly beneficial for these undeveloped areas where there is a serious shortage in health workers,” he said.

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Robotic finger is delicate enough for human contact

While rigid robotic fingers already exist, experts have raised concerns that these devices might not be up to the delicate tasks required in a doctor’s office setting. Some have pointed to potential safety issues, including a fear that overzealous robotic fingers could rupture lumps during examinations. 

More recently, scientists have developed lightweight, safe, and low-cost soft robotics that can recreate the movements of human hands. However, these devices typically haven’t been able to sense the complex properties of objects they touch the way real fingers do.

“Despite the remarkable progress in the last decade, most soft fingers presented in the literature still have substantial gaps compared to human hands,” the authors wrote. By contrast, robotic fingers have not been ready to handle real-world scenarios, they said.

To overcome this challenge, the University of Science and Technology of China developed a simple device that contains conductive fiber coils with two parts. One is a coil wound on each air chamber of the device’s bending actuators, and the other is a twisted liquid metal fiber mounted at the fingertip. This way, the device could perceive an object’s properties as effectively as human touch.

The robotic finger is designed to be soft and sensitive enough for medical diagnosis. Source: Hongbo Wang

Researchers put soft finger to the test

To test the device, the researchers started by brushing a feather against its fingertip.

“The magnified view clearly shows the resistance change, indicating its high sensitivity in force sensing,” the authors wrote.

Next, they tapped and pushed the fingertip with a glass rod and repeatedly bent the finger, observing that the device’s sensors accurately perceived the type and quantity of force they applied.

To test the finger’s medical chops, they mounted it on a robotic arm and watched as it identified three lumps embedded in a large silicone sheet, pressing on them like a doctor would. While mounted on the robotic arm, the finger also correctly located an artery on a participant’s wrist and took their pulse.

“Humans can easily recognize the stiffness of diverse objects by simply pressing it with their finger,” said the researchers. “Similarly, since the [device] has the ability to sense both its bending deformation and the force at the fingertip, it can detect stiffness similar to our human hand by simply pressing an object.”

In addition to taking pulses and examining simulated lumps, the researchers found that the robotic finger can type “like a human hand,” spelling out the word “hello.”

Additional sensors provide even more flexibility in the robotic finger’s joints. They allow the device to move in multiple directions like a human finger, so it may be ready to perform effective and efficient medical examinations in the near future, the team concluded.

“We hope to develop an intelligent, dexterous hand, together with a sensorized artificial muscle-driven robotic arm, to mimic the unparalleled functions and fine manipulations of the human hands,” said Wang.

This work was published in the Cell Press journal Cell Reports Physical Science.

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Soft Robotics spinoff Oxiptal AI’s vision tech can detect bruises on produce, excess fat on proteins, burn marks on snacks, and more. | Credit: Oxipital AI

After two straight record years during the height of the COVID-19 pandemic, industrial robot sales have crashed back to reality, especially in North America. According to industry association A3, sales of industrial robots in North America declined 30% in 2023. Sales dipped 6% in North America during Q1 2024 compared with Q1 2023.

While many analysts agree that industrial robots will eventually become ubiquitous, the timeframe for that happening remains unknown. The slowdown is partially responsible for several robotics companies shutting down or laying off staff. Mark Chiappetta, president and CEO of soft gripper maker Soft Robotics Inc., was determined to not fall into that category.

Soft Robotics today divested its soft robotic gripper business to J. Schmalz GmbH for an undisclosed amount. Glatten, Germany-based Schmalz is a leading developer of vacuum technology, making everything from suction cups and vacuum generators to complete gripping and clamping systems.

Chiappetta told The Robot Report that Schmalz is acquiring Soft Robotics’ intellectual property as well as a number of employees and facilities.

“When COVID was happening, the talk was, ‘We don’t have a choice. [Installing robots] is a matter of keeping up with demand,'” he said. “We all fully expected those buying habits to stay, which would’ve led to a tectonic shift in robotics. But those habits didn’t stay.”

Soft Robotics was founded in 2012 by Dr. George M. Whitesides of Harvard University. He envisioned the use of soft materials and microfluidics to change the way robots were made, opening the door for new markets and applications. He keynoted the inaugural Robotics Summit & Expo, produced by The Robot Report, in 2018.

Oxipital AI diversifies the business

While Soft Robotics’ grippers are now under the Schmalz umbrella, the company is no longer. It has spun off its mGripAI 3D vision and artificial intelligence technologies into a new company called Oxipital AI, for which Chiappetta holds the same job title.

Oxipital AI will focus on visual inspection tasks such as defect detection, volume estimation, SKU classification, attribute segmentation, and conveyor counting. It will also on robotic picking in various industries, starting primarily in the food business where Soft Robotics had built its reputation.

The company plans to create core object models that Chiappetta said are pre-trained using 100% synthetic data. He added that Oxipital AI requires zero imagery to be gathered, nor does it need human labeling.

A no-code feature enables customers to set rules for what constitutes a good product or bad product for inspection tasks, and a cloud-based dashboard collects and analyzes real-world data, he explained. Oxipital AI’s technology stack interfaces with all existing industrial robot arms, as well as conventional automation systems such as conveyors, said Chiappetta.

Oxipital AI is a new company focusing on AI visual inspection tasks. | Credit: Oxipital AI

Food industry forces Soft Robotics shift in focus

Besides not selling robotic grippers, the main difference from the former company is that Oxipital AI will have a major emphasis on applications that don’t use robots, he noted. For example, in the food industry, AI-based vision technologies can improve yield, increase throughput, and reduce waste, Chiapetta said.

“Food processors aren’t ready to rip out human picking lines and replace them with robotic lines,” Chiappetta said. “They are willing to put in a camera to expose how to optimize their current processes.”

Chiappetta said floor space is the biggest reason food processors aren’t adopting robotics. Most of the larger organizations, he said, are built by acquiring smaller producers. This makes every manufacturing plant different and puts floor space at a premium.

Another major problem, according to Chiappetta, is that food-processing companies are reluctant to take the initial leap of faith into robotics.

“[The food industry] hasn’t been a strong adopter of robotics to date,” he said. “Processors need to allocate capital with high interest rates, select a bidder, have a solution developed, take an existing line down, put the solution in place, have acceptance and quality done, and then they’ll know if the investment was worth it.”

“Once you take out humans, it’s hard to go back,” said Chiappetta.

He said on LinkedIn that Soft Robotics had nearly 1,000 soft grippers deployed in the field.

OnRobot is another well-known developer of soft robotic grippers. Founded in 2015, the Odense, Denmark-based company initially offered a variety of robotic end effectors.

However, it too has diversified its business by launching various sensors, tool changers, and software packages for applications such as palletizing, packaging, CNC machining, and more.

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Former Soft Robotics divests in move to AI

The Bedford, Mass.-based company has updated its website to reflect the new direction. One page details how a “leading sweet corn producer in the United States” recently implemented the AI-powered vision technology to inspect its produce for defects.

This system looks for various flaws such as missing kernels and misshapen or undersized produce. It then relays this information to the quality control team for necessary actions.

Why did Soft Robotics divest its gripper business instead of just increasing its focus on 3D vision and AI?

Chiappetta replied that this wasn’t an “adapt-or-die” situation, but that the increased cash from the divestiture certainly helps. The main benefit is to keep the company focused, he said.

“Robotic picking is really hard, and grippers are a niche business,” said Chiappetta. “The visual AI needed to do these things is the common denominator to do all these applications. And we’ve got the tech to do it.”

“Without focus, it’s difficult to survive,” he added. “I had many conversations with strategic partners and others who didn’t know how to look at a company that’s eyes (vision), hands (grippers), and brains (AI).”

Not 100% dependent on robot sales

December 2022 was the best month Soft Robotics ever hard financially, Chiappetta told The Robot Report. January is typically a slow month as companies figured out their budgets, but January 2023 was horrible, and February didn’t get any better, he said.

“Our partners were seeing the same thing,” recalled Chiappetta.

Soft Robotics last raised $26 million in November 2022. It had raised $86 million since its founding, according to Crunchbase.

Soft Robotics’ business was 100% dependent on robot sales, but Oxipital’s won’t be, Chiappetta asserted.

“The Schmalz transaction is the start of what we hope is a strategic partnership,” he said. “They have a great reputation and global distribution. It’s a natural fit for us. We need a company like Schmalz to grow soft robotic grippers. And the more soft gripping becomes the standard, the more opportunities we’ll have for our AI vision tech.”

Schmalz had developed its own soft robotic grippers, such as the OFG HYG SI-70. Source: Schmalz

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Northwestern University engineers have developed a new soft, flexible device that makes robots move by expanding and contracting — just like a human muscle.

To demonstrate their new device, called an actuator, the researchers used it to create a cylindrical, worm-like soft robot and an artificial bicep. In experiments, the cylindrical soft robot navigated the tight, hairpin curves of a narrow pipe-like environment, and the bicep was able to lift a 500-gram weight 5,000 times in a row without failing.

Because the researchers 3D-printed the body of the soft actuator using a common rubber, the resulting robots cost about $3 in materials, excluding the small motor that drives the actuator’s shape change. That sharply contrasts typical stiff, rigid actuators used in robotics, which often cost hundreds to thousands of dollars.

The new actuator could be used to develop inexpensive, soft, flexible robots, which are safer and more practical for real-world applications, researchers said.

“Roboticists have been motivated by a long-standing goal to make robots safer,” said Northwestern’s Ryan Truby, who led the study. “If a soft robot hit a person, it would not hurt nearly as much as getting hit with a rigid, hard robot. Our actuator could be used in robots that are more practical for human-centric environments. And, because they are inexpensive, we potentially could use more of them in ways that, historically, have been too cost prohibitive.”

Truby is the June and Donald Brewer Junior Professor of Materials Science and Engineering and Mechanical Engineering at Northwestern’s McCormick School of Engineering, where he directs The Robotic Matter Lab. Taekyoung Kim, a postdoctoral scholar in Truby’s lab and first author on the paper, led the research. Pranav Kaarthik, a Ph.D. candidate in mechanical engineering, also contributed to the work.

Robots that ‘behave and move like living organisms’

While rigid actuators have long been the cornerstone of robot design, their limited flexibility, adaptability and safety have driven roboticists to explore soft actuators as an alternative. To design soft actuators, Truby and his team take inspiration from human muscles, which contract and stiffen simultaneously.

“How do you make materials that can move like a muscle?” Truby asked. “If we can do that, then we can make robots that behave and move like living organisms.”

To develop the new actuator, the team 3D-printed cylindrical structures called “handed shearing auxetics” (HSAs) out of rubber. Difficult to fabricate, HSAs embody a complex structure that enables unique movements and properties. For example, when twisted, HSAs extend and expand. Although Truby and Kaarthik 3D-printed similar HSA structures for robots in the past, they were bound to using expensive printers and rigid plastic resins. As a result, their previous HSAs could not bend or deform easily.

“For this to work, we needed to find a way to make HSAs softer and more durable,” said Kim. “We figured out how to fabricate soft but robust HSAs from rubber using a cheaper and more easily available desktop 3D printer.”

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Kim printed the HSAs from thermoplastic polyurethane, a common rubber often used in cellphone cases. While this made the HSAs much softer and more flexible, one challenge remained: how to twist the HSAs to get them to extend and expand.

Previous versions of HSA soft actuators used common servo motors to twist the materials into extended and expanded states. But the researchers only achieved successful actuation after assembling two or four HSAs — each with its own motor —together. Building soft actuators in this way presented fabrication and operational challenges. It also reduced the softness of the HSA actuators.

To build an improved soft actuator, the researchers aimed to design a single HSA driven by one servo motor. But first, the team needed to find a way to make a single motor twist a single HSA.

To demo the new actuator, researchers used it to create a worm-robot that could navigate tight spaces. | Credit: Northwestern University

Simplifying ‘the entire pipeline’

To solve this problem, Kim added a soft, extendable, rubber bellows to the structure that performed like a deformable, rotating shaft. As the motor provided torque — an action that causes an object to rotate — the actuator extended. Simply turning the motor in one direction or the other drives the actuator to extend or contract.

“Essentially, Taekyoung engineered two rubber parts to create muscle-like movements with the turn of a motor,” Truby said. “While the field has made soft actuators in more cumbersome ways, Taekyoung greatly simplified the entire pipeline with 3D printing. Now, we have a practical soft actuator that any roboticist can use and make.”

The bellows added enough support for Kim to build a crawling soft robot from a single actuator that moved on its own. The pushing and pulling motions of the actuator propelled the robot forward through a winding, constrained environment simulating a pipe.

“Our robot can make this extension motion using a single structure,” Kim said. “That makes our actuator more useful because it can be universally integrated into all types of robotic systems.”

The missing piece: muscle stiffening

The resulting worm-like robot was compact (measuring just 26 centimeters in length) and crawled — both backward and forward — at a speed of just over 32 centimeters per minute. Truby noted that both the robot and artificial bicep become stiffer when the actuator is fully extended. This was yet another property that previous soft robots were unable to achieve.

“Like a muscle, these soft actuators actually stiffen,” Truby said. “If you have ever twisted the lid off a jar, for example, you know your muscles tighten and get stiffer to transmit force. That’s how your muscles help your body do work. This has been an overlooked feature in soft robotics. Many soft actuators get softer when in use, but our flexible actuators get stiffer as they operate.”

Truby and Kim say their new actuator provides yet another step toward more bio-inspired robots.

“Robots that can move like living organisms are going to enable us to think about robots performing tasks that conventional robots can’t do,” Truby said.

Editor’s Note: This article was republished from Northwestern University.

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Robotic arms in large-scale 3D printing deposit materials layer by layer, transforming production. Source: Replique

In the realm of modern manufacturing and automation, the integration of robotics continues to redefine industry standards. One technology that is pushing the boundaries of what’s possible is 3D printing. Henrike Wonneberger, co-founder and chief operating officer at Replique, explores the relationship between additive manufacturing and robotics.

Here, she highlights the technology’s impact and potential for enabling flexible, customized, and lightweight production within the automation industry.

Additive manufacturing can unlock customization

Central to the evolution of robotics is the ability to create customized components efficiently. Traditional manufacturing methods often struggle with the cost and complexity of producing bespoke parts like grippers and intricate assemblies.

Additive manufacturing enables manufacturers from small and midsize enterprises (SMEs) to large corporations to produce customized parts on-demand. With it, they can do so without the constraints of tooling or minimum order quantities.

This flexibility is enabled by the digital nature of the technology. Unlike traditional production methods such as injection molding or casting that require a lot of time and effort to set up, 3D printing allows for immediate production, as it seamlessly translates digital designs into physical objects by layering materials.

Additive manufacturing plus robotics can transform industries. Source: Replique

Optimize performance with lightweighting

Another key advantage of 3D printing in robotics lies in its capacity to optimize weight and enhance performance through innovative design. By allowing engineers to create complex geometries and hollow structures, additive manufacturing can reduce material waste and improve structural integrity.

Lighter parts can also extend the operating life of robots by reducing wear and tear on the system, thus reducing maintenance intervals. For instance, optimized grippers can further contribute to faster speed on the production line.

Lastly, thanks to lightweight construction, smaller and lighter robots can be created for heavy-duty applications. In the medium term, this approach reduces energy consumption and lowers CO2 emissions, underscoring how such production can enhance both performance and sustainability in robotics.

Humanoid robots, with their need for safety and to squeeze as much power out of batteries as possible, represent an extreme and emerging case for lightweighting.

Additive manufacturing enables the design and production of lightweight parts. Source: Replique

3D printing helps streamline assembly, enhance flexibility

Beyond customization, 3D printing can simplify assembly processes by consolidating multiple parts into single, integrated components. This approach not only reduces assembly time and inventory complexity, but it can also minimize potential points of failure and improve overall reliability.

Industries across sectors, including automotive, aerospace, and the food, can benefit from parts consolidation. In food processing, for example, additive manufacturing can reduce the number of robot joints and connection points, enhancing hygiene by minimizing places where bacteria could accumulate.

On the other hand, it also allows for dividing one part into different components, facilitating easier changes of format parts as needed.

3D printing empowers small players to create cost-effective robot parts with no minimum order quantity, starting from lot size. Source: Replique.

Robotics can benefits from agile prototyping and iterative design

The iterative nature of 3D printing accelerates the prototyping and design validation process in robotics.

Engineers can rapidly translate conceptual designs into functional prototypes, facilitating quicker design adaptions and reducing time-to-market. This enables continuous improvement and adaptation to evolving technological requirements in the robotics and automation industry.

Additive manufacturing allows for fast iterative design. Source: Replique

Get spare parts on demand for operational resilience

In operational environments where downtime is costly, the ability to produce spare parts on demand is invaluable.

3D printing enables rapid, localized production of replacement components, significantly reducing lead times and inventory costs. This capability ensures continuous operational readiness of robotic systems, enhancing overall efficiency and minimizing disruption.

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3D printing: From grippers, soft robotics to integrated systems

Format parts, such as grippers, are ideal use cases for additive manufacturing. It allows manufacturers to create these components with high customization, reduced weight and cost. Format parts can be easily optimized and changed for specific application.

For instance, a Replique collaboration with struktur.form.design Engineering GmbH, reduced the weight of a collaborative robot gripper by 78%, part count by 84%, and production costs by 30% through redesign and additive manufacturing.

Beyond that, additive manufacturing supports innovations in soft robotics, enabling the creation of flexible, adaptive structures for precise movements, e.g. in the medical prosthetics industry.

In addition, integrating sensors and electronics directly into 3D-printed components can enhance functionality and streamline assembly processes.

A soft gripper is one example of how additive manufacturing enables robotics. Source: Replique

Robots as 3D printers themselves

The synergy between robotics and 3D printing can extend beyond traditional applications. Robots themselves can act as 3D printers, expanding the scope of additive manufacturing.

In large-scale 3D printing, robotic arms can deposit materials layer by layer to create objects, enabling the production of parts on a scale previously not possible in 3D printing. This has shown promise in metalwork and construction.

Robots can be a part of 3D printers themselves. Source: Replique

Pioneer the future of robotics with 3D printing

The integration of additive manufacturing technology into robotics represents a paradigm shift in manufacturing capabilities.

From customized components to enhanced operational resilience, 3D printing enables more agility, innovation, and efficiency in the robotics and automation industry.

About the author

Henrike Wonneberger is the co-founder and chief operating officer at Replique GmbH. The Mannheim, Germany-based spinout of the BASF Digital Transformation Initiative provides an industrial 3D printing platform to enable companies to deliver on-demand parts globally through a decentralized and secure network.

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The RoboGrocery system combines vision, algorithms, and soft grippers to prioritize items to pack. Source: MIT CSAIL

As a child, I often accompanied my mother to the grocery store. As she pulled out her card to pay, I heard the same phrase like clockwork: “Go bag the groceries.” It was not my favorite task. Now imagine a world where robots could delicately pack your groceries, and items like bread and eggs are never crushed beneath heavier items. We might be getting closer with RoboGrocery.

Researchers at the Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory (MIT CSAIL) have created a new soft robotic system that combines advanced vision technology, motor-based proprioception, soft tactile sensors, and a new algorithm. RobGrocery can handle a continuous stream of unpredictable objects moving along a conveyor belt, they said.

“The challenge here is making immediate decisions about whether to pack an item or not, especially since we make no assumptions about the object as it comes down the conveyor belt,” said Annan Zhang, a Ph.D. student at MIT CSAIL and one of the lead authors on a new paper about RoboGrocery. “Our system measures each item, decides if it’s delicate, and packs it directly or places it in a buffer to pack later.

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RoboGrocery demonstrates a light touch

RoboGrocery’s pseudo market tour was a success. In the experimental setup, researchers selected 10 items from a set of previously unseen, realistic grocery items and put them onto a conveyor belt in random order. This process was repeated three times, and the evaluation of “bad packs” was done by counting the number of heavy items placed on delicate items.

The soft robotic system showed off its light touch by performing nine times fewer item-damaging maneuvers than the sensorless baseline, which relied solely on pre-programmed grasping motions without sensory feedback. It also damaged items 4.5 times less than the vision-only approach, which used cameras to identify items but lacked tactile sensing, said MIT CSAIL.

To illustrate how RoboGrocery works, let’s consider an example. A bunch of grapes and a can of soup come down the conveyor belt. First, the RGB-D camera detects the grapes and soup, estimating sizes and positions.

The gripper picks up the grapes, and the soft tactile sensors measure the pressure and deformation, signaling that they’re delicate. The algorithm assigns a high delicacy score and places them in the buffer.

Next, the gripper goes in for the soup. The sensors measure minimal deformation, meaning “not delicate,” so the algorithm assigns a low delicacy score, and packs it directly into the bin. 

Once all non-delicate items are packed, RoboGrocery retrieves the grapes from the buffer and carefully places them on top so they aren’t crushed. Throughout the process, a microprocessor handles all sensory data and executes packing decisions in real time. 

The researchers tested various grocery items to ensure robustness and reliability. They included delicate items such as bread, clementines, grapes, kale, muffins, chips, and crackers. The team also tested non-delicate items like soup cans, ground coffee, chewing gum, cheese blocks, prepared meal boxes, ice cream containers, and baking soda. 

RoboGrocery was tested in its ability to handle a range of delicate grocery items. Source: MIT CSAIL

RoboGrocery handles more varied objects than other systems

Traditionally, bin-packing tasks in robotics have focused on rigid, rectangular objects. These methods, though, can fail to handle objects of varying shapes, sizes, and stiffness. 

However, with its custom blend of RGB-D cameras, closed-loop control servo motors, and soft tactile sensors, RoboGrocery gets ahead of this, said MIT. The cameras provide depth information and color images to accurately determine the object’s shapes and sizes as they move along the conveyor belt.

The motors offer precise control and feedback, allowing the gripper to adjust its grasp based on the object’s characteristics. Finally, the sensors, integrated into the gripper’s fingers, measure the pressure and deformation of the object, providing data on stiffness and fragility.

Despite its success, there’s always room for improvement. The current heuristic to determine whether an item is delicate is somewhat crude, and could be refined with more advanced sensing technologies and better grippers, acknowledged the researchers.

“Currently, our grasping methods are quite basic, but enhancing these techniques can lead to significant improvements,” said Zhang. “For example, determining the optimal grasp direction to minimize failed attempts and efficiently handle items placed on the conveyor belt in unfavorable orientations. For example, a cereal box lying flat might be too large to grasp from above, but standing upright, it could be perfectly manageable.”

RoboGrocery is able to determine the best grasping and packing approach for each item. Source: MIT CSAIL

MIT CSAIL team looks ahead

While the project is still in the research phase, its potential applications could extend beyond grocery packing. The team envisions use in various online packing scenarios, such as packing for a move or in recycling facilities, where the order and properties of objects are unknown.

“This is a significant first step towards having robots pack groceries and other items in real-world settings,” said Zhang. “Although we’re not quite ready for commercial deployment, our research demonstrates the power of integrating multiple sensing modalities in soft robotic systems.”

“Automating grocery packing with robots capable of soft and delicate grasping and high level reasoning like the robot in our project has the potential to impact retail efficiency and open new avenues for innovation”, said senior author Daniela Rus, CSAIL director and professor of electrical engineering and computer science (EECS) at MIT.

“Soft grippers are suitable for grasping objects of various shapes and, when combined with proper sensing and control, they can solve long-lasting robotics problems, like bin packing unknown objects,” added Cecilia Laschi, Provost’s Chair Professor of robotics at the National University of Singapore, who was not involved in the work. “This is what this paper has demonstrated — bringing soft robotics a step forward towards concrete applications.”

“The authors have addressed a longstanding problem in robotics — the handling of delicate and irregularly-shaped objects — with a holistic and bioinspired approach,” said Robert Wood, a professor of electrical engineering at Harvard University who was not involved in the paper. “Their use of a combination of vision and tactile sensing parallels how humans accomplish similar tasks and, importantly, sets a benchmark for performance that future manipulation research can build on.”

Zhang co-authored the paper with EECS Ph.D. student Valerie K. Chen ’22, M.Eng. ’23; Jeana Choi ’21, M.Eng. ‘22; and Lillian Chin ‘17 SM, ’19 Ph.D. ’23, currently assistant professor at the University of Texas at Austin. The researchers presented their findings at the IEEE International Conference on Soft Robotics (RoboSoft) earlier this year.

About the author

Rachel Gordon is senior communications manager at MIT CSAIL. This article is reposted with permission.

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The Gentle Duo Mini soft grippers are suitable for food handling. Source: Ubiros

Reliable and delicate robotic grasping has long been a challenge, but Ubiros Inc. said it can solve the problem without the bulky pneumatics or complex coding of previous approaches.

The company’s electrically driven and modular grippers are easier to deploy and use than other soft grippers, according to Onder Ondemir, president of Ubiros. He is also a professor in the engineering department at Northeastern University.

Ubiros offers the Gentle Flex Module and Gentle Flex CC for users that want to build their own grippers. The spinout of Worcester Polytechnic Institute (WPI) also offers Gentle Duo with two soft fingers and Gentle Pro with four fingers, as well as Touch models for both that include force control, part sensing/confirmation, and a low-power mode.

The Natick, Mass.-based company said its compliant grippers are suitable for food handling and packaging, textiles, and some warehouse applications. Ubiros, which is a resident startup at MassRobotics, already has distributor agreements in China, South Korea, Turkey, the U.K., and the U.S.

Founder fascinated by technology

“I loved Knight Rider as a kid and asked my mom, ‘Who makes [smart car] KITT? What do I need to become to make something like that?’” recalled Ondemir. “She said ‘mechanical engineer.’”

“I grew up and became an industrial engineer, but I’ve always been fascinated with mechanical systems,” he told The Robot Report. “I worked at an insurance company modeling the capacity of systems and demand forecasts, and I became a vice president.”

Ondemir later ran into a friend [Cagdas Onal] who was doing post-doctoral associate work at MIT and collaborating with Harvard University researchers working on soft robotics.

“We knew the limitations of pneumatics – precision is low, control is hard, and the equipment needed to generate compressed air uses a lot of electricity,” he said. “After spending years in the lab, one day, he came to me and said, ‘Onder, I think I have groundbreaking technology for packaging and gripping in general, but I don’t want to run the company.’ Becoming the CEO was a no-brainer to fulfill my desire to build machines and work with my friend.”

Ubiros President Onder Ondemir at MassRobotics. Source: Ubiros

The genesis of Ubiros

Ubiros has largely focused on food handling.

Automating the handling of fruit and baked goods is not easy, because such delicate food items are easily damaged, Ondemir noted. Rigid grippers typically don’t have sufficient sensitivity, and many other companies are trying to solve problems such as object detection and singulation, he said.

“In farming, crops are being left to rot in the field, which is a huge waste,” added Ondemir. “A key barrier to getting automation is handling food with a soft touch.”

“But the real problem we’re solving is the worker shortage,” he said. “Harvesting and packing jobs aren’t interesting to people, and there are the issues of efficiency for the employer – most people work one shift – and also injuries and finally the cost of food.”

Fingers versus suction cups

For most applications where similar items are picked, suction cups are sufficient, said Ondemir. However, when there’s clutter, such as in e-commerce bins, or tight spaces like bookshelves, picking then requires a combination of sensing to identify each object and the ability to singulate that object.

Suction cups are versatile, but porous, dusty, fragile, or oddly weighted items are not always suitable for suction cups, Ondemir observed.

“We’re proud to be one of the few companies developing individual finger actuation rather than the whole hand,” he said. “For singulation, it can provide alternatives in complex picking situations.”

Ubiros Gentle grippers promise benefits

Ubiros’ Gentle grippers use electric servo motors and a cable-driven system similar to the tendons of the human hand.

“The difficulty in designing the system was maintaining softness while mechanically operating the finger – that’s where our patent is,” Ondemir said. “Our technology allows the finger unit to be flexible in the grasping direction but very rigid in twisting or bending sideways.”

Electric end-of-arm tooling (EOAT) removes the need for tubes, valves, and compressors, he said. Also, while pneumatic systems need to cycle to attempt another grasp, an electric one can reposition more quickly.

In addition, electric grippers have instant torque rather than needing to build up pressure for heavy payloads as hydraulic or pneumatic systems do, said Ondemir.

Up to 35% of the electricity bill in factories is spent on pressurized air, and 40% of the battery life of a mobile manipulator is consumed by a suction cup, he asserted. Thus, Ubiros’ grippers could save a lot of battery power for autonomous mobile robots (AMRs) or drones, Ondemir said.

Is Ubiros looking at mobile manipulation?

“We’ve had serious conversations with Staubli,” Ondemir replied. “Our gripper would be in addition to its existing arm and base, unlike others.”

In addition, United Robotics Group has integrated Ubiros’ gripper with a mobile manipulator that will be demonstrated at Automate.

Ondemir surveys tech trends

Beyond mobile manipulation, Ondemir relied on his experience in robotics development to comment on current tech trends.

“Artificial intelligence and machine learning allow us to implement predictive maintenance,” he said. “Our electrical micro-controller is partly a system for force control and partly sensing. It’s able to collect temperature data from inside the gripper, plus cycle counts and electric current to build models to predict failures. That’s in our roadmap.”

Ubiros is not currently working with digital twins because it’s difficult to know the actual deflection of soft objects and where something is in space, acknowledged Ondemir. A lot of research is being devoted to this topic, he said.

What about humanoids? “They’ll have to have soft components for safety and to guard against falling,” Ondemir said. “This will be a key use for soft robotics in general, not just soft grippers. Because we’re a spinoff of WPI, we already have soft 3D sensors and a patented design of a soft arm, but there’s a lot still to do.”

Mechanical intelligence for manipulation

“The idea behind what we call ‘mechanical intelligence’ is that if you can mechanically achieve something, you need expensive programming, motion control, and vision less,” said Ondemir. “We built something that is under-actuated, with fewer motors to move the joints. Electric actuation allows us to have full-bodied fingers rather than hollow ones that can be punctured or leak in otherwise sanitary environments.”

“Depending on the shape of the object, the gripper can automatically conform to it. It’s more forgiving of inaccuracies, and you don’t need extreme precision,” he continued. “Because the grippers bend themselves over an object like an egg or an apple, the force is distributed over a larger area.”

Ubiros did build some force control into its Gentle grippers, allowing users to increase or decrease pressure, but it’s not necessary in most cases, Ondemir said.

Ubiros and its partners recently participated in a MassRobotics Demo Day. Source: Ubiros

Ubiros looks ahead

The Gentle gripper is initially tackling labeling and grading of tomatoes and cucumbers, and Ubiros has received a lot of interest recently from bakeries, said Ondemir. A hygienic gripper could then address handling of raw beef, poultry, and fish.

To that end, Ubiros is looking for funding to make its grippers more hygienic and robust against cleaning agents. It is working on safe-food handling certifications.

On the industrial side, Ubiros is conducting a pilot with Mitsubishi to handle a variety of objects and manage robot grasping through Mitsubishi’s teach pendant.

“Down the road a few years, we want to focus more on the data side, allowing customers to access data through the end effectors,” Ondemir said. “We plan to eventually bring other patented technologies into the workplace – 3D sensors, haptic gloves, human-in-the-loop systems, remote manipulation, and soft arms.”

Ubiros will be at the Robotics Summit & Expo next week and Automate the week after that.

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Project CETI is a nonprofit scientific and conservation initiative that aims to decode whale communications. | Source: Project CETI

Off the idyllic shores of Dominica, a country in the Caribbean, hundreds of sperm whales gather deep in the sea. While their communication sounds like a series of clicks and creaks to the human ear, these whales have unique, regional dialects and even accents. A multidisciplinary group of scientists, led by Project CETI, is using soft robotics, machine learning, biology, linguistics, natural language processing, and more to decode their communications. 

Founded in 2020, Project CETI, or the Cetacean Translation Initiative, is a nonprofit organization dedicated to listening to and translating the communication systems of sperm whales. The team is using specially created tags that latch onto whales and gather information for the team to decode. Getting these tags to stay on the whales, however, is no easy task. 

“One of our core philosophies is we could never break the skin. We can never draw blood. These are just our own, personal guidelines,” David Gruber, the founder and president of Project CETI, told The Robot Report. 

“[The tags] have four suction cups on them,” he said. “On one of the suction cups is a heart sensor, so you can get the heart rate of the whale. There’s also three microphones on the front of it, so you hear the whale that it’s on, and you can know the whales that’s around it and in front of it.

“So you’ll be able to know from three different microphones the location of the whales that are speaking around it,” explained Gruber. “There’s a depth sensor in there, so you can actually see when the whale was diving and so you can see the profiles of it going up and down. There’s a temperature sensor. There’s an IMU, and it’s like a gyroscope, so you can know the position of the whale.”

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Finding a humane way to tag whales

One of the core principles of Project CETI, according to Gruber, is to use technology to bring people closer to animals. 

“There was a quote by Stephen Hawking in a BBC article, in which he posited that the full development of AI and robotics would lead to the extinction of the human race,” Gruber said. “And we thought, ‘This is ridiculous, why would scientists develop something that would lead to our own extinction?’ And it really inspired us to counter this narrative and be like, ‘How can we make robots that are actually very gentle and increase empathy?’”

“In order to deploy those tags onto whales, what we needed was a form of gentle, stable, reversible adhesion,” Alyssa Hernandez, a functional morphologist, entomologist, and biomechanist on the CETI team, told The Robot Report. “So something that can be attached to the whale, where it would go on and remain on the whale for a long amount of time to collect the data, but still be able to release itself eventually, whether naturally by the movements of the whale, or by our own mechanism of sort of releasing the tag itself.”

This is what led the team to explore bio-inspired techniques of adhesion. In particular, the team settled on studying suction cups that are common in marine creatures. 

“Suction discs are pretty common in aquatic systems,” said Hernandez. “They show up in multiple groups of organisms, fish, cephalopods, and even aquatic insects. And there are variations often on each of these discs in terms of the morphology of these discs, and what elements these discs have.”

Hernandez was able to draw on her biology background to design suction-cup grippers that would work particularly well on sperm whales that are constantly moving through the water. This means the suction cup would have to withstand changing pressures and forces. They can stay on a whale’s uneven skin even when it’s moving. 

“In the early days, when we first started this project, the question was, ‘Would the soft robots even survive in the deep sea?’” said Gruber. 

An overview of Project CETI’s mission. | Source: Project CETI

How suction cup shape changes performance

“We often think of suction cups as round, singular material elements, and in biology, that’s not usually the case,” noted Hernandez. “Sometimes these suction disks are sort of elongated or slightly different shaped, and oftentimes they have this sealing rim that helps them keep the suction engaged on rough surfaces.”

Hernandez said the CETI team started off with a standard, circular suction cup. Initially, the researchers tried out multiple materials and combinations of stiff backings and soft rims. Drawing on her biology experience, Hernandez began to experiment with more elongated, ellipse shapes. 

“I often saw [elongated grippers] when I was in museums looking at biological specimens or in the literature, so I wanted to look at an ellipse-shaped cup,” Hernandez said. “So I ended up designing one that was a medium-sized ellipse, and then a thinner ellipse as well. Another general design that I saw was more of this teardrop shape, so smaller at one end and wider at the base.” 

Hernadez said the team also looked at peanut-shaped grippers. In trying these different shapes, she looked for one that would provide increased resistance over the more traditional circular suction cups. 

“We tested [the grippers] on different surfaces of different roughness and different compliance,” recalled Hernandez. “We ended up finding that compared to the standard circle, and variations of ellipses, this medium-sized ellipse performed better under shear conditions.” 

She said the teardrop-shaped gripper also performed well in lab testing. These shapes performed better because, unlike a circle, they don’t have a uniform stiffness throughout the cup, allowing them to bend with the whale as it moves. 

“Now, I’ve modified [the suction cups] a bit to fit our tag that we currently have,” Hernandez said. “So, I have some versions of those cups that are ready to be deployed on the tags.”

Project CETI uses drones to monitor sperm whale movements and to place the tags on the whales. | Source: Project CETI

Project CETI continues iterating

The Project CETI team is actively deploying its tags using a number of methods, including having biologists press them onto whales using long poles, a method called pole tagging, and using drones to press the tags onto the whales. 

Once they’re on the whale, they stay on for anywhere from a few hours to a few days. Once they fall off, the CETI team has a mechanism that allows them to track the tags down and pull all of the gathered data off of them. CETI isn’t interested in making tags that can stay on the whales long-term, because sperm whales can travel long distances in just a few days, and it could hinder their ability to track the tags down once they fall off. 

The CETI team said it plans to continue iterating on the suction grippers and trying new ways to gently get crucial data from sperm whales. It’s even looking into tags that would be able to slightly crawl to different positions on the whale to gather information about what the whale is eating, Gruber said. The team is also interested in exploring tags that could recharge themselves. 

“We’re always continuing to make things more and more gentle, more and more innovative,” said Gruber. “And putting that theme forward of how can we be almost invisible in this project.”

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While humanoid robots have burst into mainstream attention in the past year, and more and more companies have released their own models, many operate similarly. The typical humanoid uses arms and grippers to handle objects, and their rigid legs provide a mode of transportation. Researchers at the Toyota Research Institute, or TRI, said they want to take humanoids a step further with the Punyo robot. 

Punyo isn’t a traditional humanoid robot in that it doesn’t yet have legs. So far, TRI‘s team is working with just the torso of a robot and developing manipulation skills. 

“Our mission is to help people with everyday tasks in our homes and elsewhere,” said Alex Alspach, one of TRI’s tech leads for whole-body manipulation, in a video (see above). “Many of these manipulation tasks require more than just our hands and fingers.” 

When humans have to carry a large object, we don’t just use our arms to carry it, he explained. We might lean the object against our chest to lighten the load on our arms and use our backs to push through doors to reach our destination.

Manipulation that uses the whole body is tricky for humanoids, where balance is a major issue. However, the researchers at TRI designed its robot to do just that. 

“Punyo does things differently. Taking advantage of its whole body, it can carry more than it could simply by pressing with outstretched hands,” added Andrew Beaulieu, one of TRI’s tech leads for whole-body manipulation. “Softness, tactile sensing, and the ability to make a lot of contact advantageously allow better object manipulation.” 

TRI said that the word “punyo” is a Japanese word that elicits the image of a cute yet resilient robot. TRI’s stated goal was to create a robot that is soft, interactive, affordable, safe, durable, and capable.

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Robot includes soft limbs with internal sensors

Punyo’s hands, arms, and chest are covered with compliant materials and tactile sensors that allow it to feel contact. The soft materials allow the robot’s body to conform with the objects it’s manipulating.

Underneath it are two “hard” robot arms, a torso frame, and a waist actuator. TRI says it aimed to combine the precision of a traditional robot with the compliance, impact resistance, and sensing simplicity of soft robotic systems. 

The entirety of Punyo’s arms are covered in air-filled bladders or bubbles. These bubbles connect via a tube to a pressure sensor. This sensor can feel forces applied to the outer surfaces of the bubble.

Each bubble can also be individually pressurized to a desired stiffness, and add around 5 cm of compliance to the surface of the robot’s arms. 

Instead of traditional grippers, Punyo has “paws” made up of a single high-friction latex bubble with a camera inside. The team printed the inside of these bubbles with a dot pattern. The camera watches for deformities in this pattern to estimate forces. 

Left: Under Punyo’s sleeves are bubbles, air tubes, and pressure sensors that add compliance and tactile sensing to the arms. Right: Closeup of a pair of arm bubbles. | Source: Toyota Research Institute

Punyo learns to use full-body manipulation

Punyo learned contact-rich policies using two methods: diffusion policy and example-guided reinforcement learning. TRI announced its diffusion policy method last year. With this method, the robot uses human demonstrations to learn robust sensorimotor policies for hard-to-model tasks.

Example-guided reinforcement learning is a method that requires tasks to be modeled in simulation and with a small set of demonstrations to guide the robot’s exploration. TRI said it uses this kind of learning to achieve robust manipulation policies for tasks it can model in simulation. 

When the robot can see demonstrations of these tasks it can more efficiently learn them. It also gives TRI team more room to influence the style of motion the robot uses to achieve the task.

The team uses adversarial motion priors (AMP), which are traditionally used for stylizing computer-animated characters, to incorporate human motion imitation into its reinforcement pipeline. 

Reinforcement learning does require the team to model tasks in simulation for training. To do this, TRI used a model-based planner for demonstrations instead of teleoperation. It called this process “plan-guided reinforcement learning.”

TRI claimed that using a planner makes longer-horizon tasks that are difficult to teleoperate possible. The team can also automatically generate any number of demonstrations, reducing its pipeline’s dependence on human input. This moves TRI closer to scaling up the number of tasks tha tPunyo can handle. 

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Researchers at the University of Southern California (USC) have developed a design for a sensor inspired by the folding patterns of origami that uses 3D electrodes to track deformation in robots.

The project was led by Hangbo Zhao, who holds dual appointments as an assistant professor in the Department of Aerospace and Mechanical Engineering and the Alfred E. Mann Department of Biomedical Engineering. Zhao wanted to find a new way to measure stretchability.

Typically, stretchability and recovery, which are crucial metrics for predicting and controlling the motion of a robot, are measured using cameras. This process, however, doesn’t work well outside of a lab, as when robots are out in the world, operating in space, or within the human body, they can’t be surrounded by multiple cameras.

Additionally, soft robots that stretch and deform are typically made of a soft material like rubber. While these materials are good at stretching, they can also undergo irreversible changes in the material properties through repeated use.

Instead of using cameras and soft materials, Zhao and his team leveraged their previous work in the designs and manufacturing of small-scale 3D sculptures that apply principles of origami. These methods allowed them to create a sensor that can measure a strain range up to three times higher than a typical sensor.

To do this, the USC team built a 3D structure of electrodes that converts stretch and release to a process of folding and unfolding. This process allows the shape of the robot to change without transforming the substance of the material itself.

As these electrodes unfold, they capture the strength of the electrical field. The team then developed a model that converts this electrical field reading into a measurement of deformation. This method allows the sensors to be used repeatedly and to give precise readings even when measuring large and dynamic deformations of soft bodies.

This approach is best suited for responding to large deformations that existing sensors aren’t capable of identifying accurately. This is because, through folding, engineers can achieve large jumps in dimensions without causing a change in material.

“We integrate the 3D origami-inspired electrodes with a soft, stretchable substrate through covalent bonding,” Zhao said. “This unique combination allows us to measure a very large deformation, as much as 200 percent strain, with an ultra-low hysteresis of around 1.2 percent. There’s also a very fast response, within 22 milliseconds.”

These sensors can be attached to soft bodies in motion, which includes anything from mechanical tendons found in prosthetic legs to human internal organs.

The high-performing design of these sensors means they are capable of rapidly measuring high deformation with maximum precision. The sensors also have a sensing area of just a few square millimeters, which allowed the team to measure deformation locally. The sensors can also detect strain from different directions.

While these sensors were designed for controlling soft robotics, they can also be suited for innovations in biomedicine.

“We can apply these sensors as wearable or implantable biomedical devices for healthcare monitoring,” Zhao said. “For example, tracking the movement and flexibility of our skin or our joints. There’s also high demand for developing implantable sensors that can continuously monitor the functional status of internal organs that undergo cyclic expansion and contraction.”

The USC team’s paper, “High-Stretchability and Low-Hysteresis Strain Sensors Using Origami-Inspired 3D Mesostructures,” was published in the journal Science Advances.

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Soft Robotics grippers can move items that might be damaged by classic mechanical grippers. | Credit: Soft Robotics

Robots are finally getting a grip. 

Developers have been striving to close the gap on robotic gripping for the past several years, pursuing applications for multibillion-dollar industries. Securely gripping and transferring fast-moving items on conveyor belts holds vast promise for businesses. 

Soft Robotics, a Bedford, Mass. startup, is harnessing NVIDIA Isaac Sim to help close the sim-to-real gap for a handful of robotic gripping applications. One area is perfecting gripping for pick and placement of foods for packaging. 

Food packaging and processing companies are using the startup’s mGripAI system which combines soft grasping with 3D Vision and AI to grasp delicate foods such as proteins, produce, and bakery items without damage.

“We’re selling the hands, the eyes and the brains of the picking solution,” said David Weatherwax, senior director of software engineering at Soft Robotics. 

Unlike other industries that have adopted robotics, the $8 trillion food market has been slow to develop robots to handle variable items in unstructured environments, says Soft Robotics. 

The company, founded in 2013, recently landed $26 million in Series C funding from Tyson Ventures, Marel and Johnsonville Ventures.

Companies such as Tyson Foods and Johnsonville are betting on the adoption of robotic automation to help improve safety and increase production in their facilities. Both companies rely on Soft Robotics technologies. 

Soft Robotics is a member of the NVIDIA Inception program, which provides companies with GPU support and AI platform guidance. 

Getting a grip with synthetic data

Soft Robotics develops unique models for every one of its gripping applications, each requiring specific data sets. And picking from piles of wet, slippery chicken and other foods can be a tricky challenge. 

Utilizing Omniverse and Isaac Sim, the company can create 3D renderings of chicken parts with different backgrounds, like on conveyor belts or in bins and with different lighting scenarios. 

The company taps into Isaac Replicator to develop synthetic data, generating hundreds of thousands of images per model and distributing that among an array of instances in the cloud. Isaac Replicator is a set of tools, APIs, and workflows for generating synthetic data using Isaac Sim.

It also runs pose estimation models to help its gripping system see the angle of the item to pick. 

NVIDIA A100 GPUs on site enable Soft Robotics to run split-second inference with the unique models for each application in these food-processing facilities. Meanwhile, simulation and training in Isaac Sim offer access to NVIDIA A100s for scaling up workloads.

“Our current setup is fully synthetic, which allows us to rapidly deploy new applications. We’re all in on Omniverse and Isaac Sim, and that’s been working great for us,” said Weatherwax. 

Solving issues with occlusion, lighting 

A big challenge at Soft Robotics is solving issues with occlusion for an understanding of how different pieces of chicken stack up and overlap one another when dumped into a pile. “How those form can be pretty complex,” Weatherwax said.

Glares on wet chicken can potentially throw off detection models. “A key thing for us is the lighting, so the NVIDIA RTX-driven ray tracing is really important,” he said. 

The glares on wet chicken is a classic lighting and vision problem that requires a new approach for training machine learning vision models. | Credit: Soft Robotics

But where it really gets interesting is modeling it all in 3D and figuring out in a split second which item is the least obstructed in a pile and most accessible for a robot gripper to pick and place. 

Building synthetic data sets with physics-based accuracy, Omniverse enables Soft Robotics to create such environments. “One of the big challenges we have is how all these amorphous objects form into a pile,” Weatherwax said. 

Boosting production line pick accuracy

Production lines in food processing plants can move fast. But robots deployed with application-specific models promise to handle as many as 100 picks per minute. 

Still a work in progress, success in such tasks hinges on accurate representations of piles of items, supported by training data sets that consider every possible way items can fall into a pile. 

The objective is to provide the robot with the best available pick from a complex and dynamic environment. If food items fall off the conveyor belt or otherwise become damaged then it is considered waste, which directly impacts yield.

Driving production gains 

Meat-packing companies rely on lines of people for processing chicken, but like so many other industries they have faced employee shortages. Some that are building new plants for food processing can’t even attract enough workers at launch, said Weatherwax. 

“They are having a lot of staffing challenges, so there’s a push to automate,” he said.

The Omniverse-driven work for food processing companies has delivered a more than 10X increase in its simulation capacity, accelerating deployment times for AI picking systems from months to days. 

And that’s enabling Soft Robotics customers to get a grip on more than just deploying automated chicken-picking lines — it’s ensuring that they are covered for an employment challenge that has hit many industries, especially those with increased injury and health risks. 

“Handling raw chicken is a job better suited for a robot,” he said.

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This week, we have a special video edition of The Robot Report podcast. This is the video feed from our recent interview with Tatum Robotics founder and CEO, Samantha Johnson. The video features American Sign Language (ASL) translation so that hearing-impaired individuals can also enjoy the content.

Tatum Robotics is building a robotic device shaped like a human hand and arm, that can mimic a human translator for deafblind individuals. Currently, deafblind individuals communicate by touching the hand of their translator. The human translator uses finger spelling and ASL signs to communicate.

Tatum Robotics is building a robotic analog to the human hand, designed to replicate the interaction between a translator and a deafblind user. Ultimately, Tatum Robotics wants to open up the world of ebooks for consumption by deafblind individuals. This will be followed by remote communication (i.e. over the web) between both hearing individuals and deafblind individuals, or even between two deafblind individuals.

As Samantha Johnson discusses in the video, until now, deafblind individuals are often isolated and bored for long periods of time, with no ability to communicate without a translator.

An early prototype of the Tatum Robotics communication robot for deafblind individuals. | Credit: Tatum Robotics

We want to thank the ASL translators on this project: Tymber Marsh and Sean Havas for their amazing translation skills. Tatum Robotics is currently recruiting additional ASL signers to contribute their unique ASL techniques to the robot design. If you are interested, contact Tatum Robotics directly for how you can contribute.

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Soft Robotics’ mGrip soft gripper can handle even delicate foods, like cupcakes, without smashing them. | Source: Soft Robotics

Soft Robotics brought in $26 million in the first closing of its Series C funding round. This brings the robotic picking company’s total funding to $86 million, according to Crunchbase. 

Soft Robotics plans to use the latest round of funding to expand commercial deployments of mGripAI, its robotic picking product. mGripAI is an IP69K-rated automation package that uses 3D vision and artificial intelligence (AI) to allow industrial arms to perform automated bulk picking in food processes. 

mGripAI, originally brought to market in 2021, can perform over 90 picks per minute. The system includes perception modules that capture high-resolution, 3D images. These images are sent to an intelligence module, which translates them into action for the robotic arm and gripper. The mGrip soft gripper works in unison with the intelligence module to pick the product. 

The mGripAI system is able to track objects in real time for maximum pick accuracy. The system is also capable of grasp optimization, intelligent robot motion control and embedded object understanding. 

“We’re delighted that some of the world’s leaders in the food production and automation markets have decided to join existing investors in supporting SRI’s continuing growth journey,” Jeff Beck, CEO of Soft Robotics, said. “SRI’s technologies are increasingly crucial to enabling and scaling efficient and safe production of several food categories. This round of growth capital strengthens SRI’s ability to rapidly develop, deploy and support those technologies.”

Tyson Ventures, the venture capital arm of Tyson Foods and an existing Soft Robotics customer, led the funding round. Marel and Johnsonville, another Soft Robotics customer, also joined the funding round as new investors. The round also included participation from the company’s existing investors. 

“At Tyson, we are continually exploring new areas in automation that can enhance safety and increase the productivity of our team members,” Rahul Ray, Senior Director of Tyson Ventures, said. “Soft Robotics’ revolutionary robotic technology, computer vision and AI platform have the potential to transform the food industry and will play a key role in any company’s automation journey.”

While the company has primarily focused on automated bulk food processes, in May, Soft Robotics announced that it will be expanding the commercial focus of its mGripAI, a soft gripping solution for automating bulk food picking processes. The company plans to make the product available for order fulfillment, sortation, decanting and kitting. 

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Harvard’s tentacle-like gripper wrapping around a succulent. | Source: Harvard Microrobotics Lab/Harvard SEAS

Researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have created a tentacle-like gripper that can grasp irregularly shaped or soft objects without damaging them. 

The gripper is made up of many thin, soft tentacles that rely on inflation to wrap themselves around an object without any sensing, planning or feedback control. Individually, each tentacle is too weak to pick up many objects, but with many working together the gripper can gently lift heavy and oddly shaped objects.

Each tentacle is made up of a foot-long hollow, rubber rubes. The tubes are made with thicker plastic on one side, so that was the tube is pressurized it curls like a pigtail. As the tube curls, it wraps and entangles itself around an object. Each added tentacle increases the strength of this hold. The gripper releases the object by simply depressurizing the tentacles. 

When designing the gripper, the research team took inspiration from nature. The gripper’s tentacles act similarly to how a jellyfish stuns its prey. 

To test how effective the gripper was, the research team used simulation and experiments where the gripper was tasked with handling a range of objects, including different houseplants and toys. 

The team hopes that the gripper can be used to grasp fragile objects, like soft fruits and vegetables in agricultural production and distribution and delicate tissue in medical settings, as well as irregularly shaped objects, like glassware, in warehouses. The gripper could replace traditional grippers that rely on embedded sensors, complex feedback loops and advanced machine-learning algorithms to work. 

The team’s research was published in the Proceedings of the National Academy of Sciences (PNAS). It was co-authored by Clark Teeple, Nicholas Charles, Yeonsu Jung, Daniel Baum and James C. Weaver, and supported by the Office of Naval Research, the National Science Foundation, the Simons Foundation and the Henri Seydoux fund. 

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MIT researchers have created a 3D printed material with embedded sensors that can sense how its moving. | Source: MIT/CSAIL

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed programmable materials that can sense their own movements. The team created lattice materials with networks of air-filled channels, which allows researchers to measure changes in air pressure within the channels when the material is being moved or bent. 

The lattice structure created by the team is a kind of architected material, meaning when you change the geometry of the features in the material, its mechanical properties, like stiffness or toughness, are altered. For a lattice, the denser the network of cells making up the structure, the stiffer it is. 

It’s difficult to integrate sensors into these materials because of the sparse, complex shapes that make them up. Putting sensors outside the structure, however, doesn’t provide enough information to get a complete picture of how the material is deforming or moving. 

CSAIL’s team used digital light processing 3D printing to incorporate the air-filled channels into the struts that form the lattice structure of the team’s material. The researchers drew the structure out of a pool of resin and hardened it into a precise shape using projected light. In this method, an image is projected onto the wet resin, and areas struct by the light are cured. Researchers used pressurized air, a vacuum and intricate cleaning to remove any excess resin before it was cured.

When the resulting structure is moved or squeezed, the channels formed by the 3D printing are deformed, causing the volume of air inside to change. The team used an off-the-shelf pressure sensor to measure these changes in pressure and get feedback on how the material is deforming. 

The CSAIL team then built off of their results by building sensors into a class of materials developed for motorized soft robotics called handed shearing auxetics (HSAs). HSAs can be twisted or stretched, making them good for soft robotic actuators. Like architected materials, HSAs are difficult to embed sensors into because of their complex structure. 

The team ran the sensorized HSA material through a series of movements for over 18 hours, and used the sensor data they gathered to train a neural network to accurately predict the robot’s motion. 

In the future, the team hopes its technology could be used to create soft, flexible robots with embedded sensors. These robots could understand their own posture and movements. The CSAIL team also sees potential for their technology to be used to create wearable devices that provide feedback on how the user is moving or interacting with their environment. 

The team recently published the results of their study in Science Advances. Daniela Rus, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science and director of CSAIL, was the lead author on the paper. Co-authors included Lillian Chin, a graduate student at MIT CSAIL, Ryan Truby, former CSAIL postdoc and now assistant professor at Northwestern University, and Annan Zhang, CSAIL graduate student. 

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The GelSight fin ray gripper was able to feel the pattern on Mason jars. | Source: CSAIL

A research team at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed a robotic gripper with Fin Ray fingers that are able to feel the objects it manipulates. 

The Perceptual Science Group at CSAIL, led by professor Edward Adelson and Sandra Liu, a mechanical engineering PhD student, created touch sensors for their gripper, allowing it to feel with the same or more sensitivity as human skin. 

The team’s gripper is made of two Fin Ray fingers. The fingers act similar to a fish’s tail, which will bend towards an applied force rather than away, and are 3D printed from a flexible plastic material. Typical Fin Ray grippers have cross-struts that run through the interior, but the CSAIL team decided to hollow out the interior to make room for their sensory components. 

The inside of the gripper is illuminated by LEDs. On one end of the hollowed-out gripper sits a camera mounted to a semi-rigid backing. The camera faces a layer of pads made of a silicone gel called GelSight. The layer of pads is glued to a thin sheet of acrylic material, which is attached to the opposite end of the inner cavity. 

The gripper is designed to fold seamlessly around the objects it grips. The camera determines how the silicone and acrylic sheets deform as it touches an object. From these observations, the camera, with computational algorithms, can figure out the general shape of the object, how rough its surface is, its orientation in space and the force being applied by, and imparted to, each finger. 

Using this method, the gripper was able to handle a variety of objects, including a mini-screwdriving, a plastic strawberry, an acrylic paint tube, a Ball Mason jar and a wine glass. 

While holding these objects, the gripper was able to detect fine details on their surfaces. For example, on the plastic strawberry, the gripper could identify individual seeds on its surface. The fingers could also feel the lettering on the Mason jar, something that vision-based robotics struggle with because of the way glass objects refract light. 

Additionally, the gripper could squeeze a paint tube without breaking the container and spilling its contents, and pick up and put down a wine glass. The gripper could sense when the base of the glass touched the tabletop, resulting in proper placement seven out of 10 times. 

The team hopes to improve on the sensor by making the fingers stronger. By removing the cross-struts, the team also removed much of the structural integrity, meaning the fingers have a tendency to twist while gripping things. The CSAIL team also want to create a three fingered gripper that could pick up fruits and vegetables and evaluate their ripeness. 

The team’s work was presented at the 2022 IEEE 5th International Conference on soft robotics. 

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