Engineers TV

As a member of Engineers Ireland you have access to Engineers TV, which contains presentations, technical lectures, courses and seminar recordings as well as events, awards footage and interviews.

Flexible sensors and an artificial intelligence model tell deformable robots how their bodies are positioned in a 3D environment.

For the first time, MIT researchers have enabled a soft robotic arm to understand its configuration in 3D space, by leveraging only motion and position data from its own 'sensorised' skin.

Soft robots constructed from highly compliant materials, similar to those found in living organisms, are being championed as safer, and more adaptable, resilient, and bioinspired alternatives to traditional rigid robots.

But giving autonomous control to these deformable robots is a monumental task because they can move in a virtually infinite number of directions at any given moment. That makes it difficult to train planning and control models that drive automation.

Large systems of multiple motion-capture cameras


Traditional methods to achieve autonomous control use large systems of multiple motion-capture cameras that provide the robots feedback about 3D movement and positions. But those are impractical for soft robots in real-world applications.

In a paper being published in the journal 'IEEE Robotics and Automation Letters', the researchers describe a system of soft sensors that cover a robot’s body to provide 'proprioception' — meaning awareness of motion and position of its body.

That feedback runs into a novel deep-learning model that sifts through the noise and captures clear signals to estimate the robot’s 3D configuration.

The researchers validated their system on a soft robotic arm resembling an elephant trunk, that can predict its own position as it autonomously swings around and extends.

The sensors can be fabricated using off-the-shelf materials, meaning any lab can develop their own systems, says Ryan Truby, a postdoc in the MIT Computer Science and Artificial Laboratory (CSAIL) who is co-first author on the paper along with CSAIL postdoc Cosimo Della Santina.

“We’re sensorising soft robots to get feedback for control from sensors, not vision systems, using a very easy, rapid method for fabrication,” he says.

“We want to use these soft robotic trunks, for instance, to orient and control themselves automatically, to pick things up and interact with the world. This is a first step towards that type of more sophisticated automated control.”

One future aim is to help make artificial limbs that can more dexterously handle and manipulate objects in the environment.

“Think of your own body: you can close your eyes and reconstruct the world based on feedback from your skin,” says co-author Daniela Rus, director of CSAIL and the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science. “We want to design those same capabilities for soft robots.”

Shaping soft sensors


A longtime goal in soft robotics has been fully integrated body sensors. Traditional rigid sensors detract from a soft robot body’s natural compliance, complicate its design and fabrication, and can cause various mechanical failures.

Soft-material-based sensors are a more suitable alternative, but require specialised materials and methods for their design, making them difficult for many robotics labs to fabricate and integrate in soft robots.

While working in his CSAIL lab one day looking for inspiration for sensor materials, Truby made an interesting connection. “I found these sheets of conductive materials used for electromagnetic interference shielding, that you can buy anywhere in rolls,” he says.

These materials have 'piezoresistive' properties, meaning they change in electrical resistance when strained. Truby realised they could make effective soft sensors if they were placed on certain spots on the trunk.

As the sensor deforms in response to the trunk’s stretching and compressing, its electrical resistance is converted to a specific output voltage. The voltage is then used as a signal correlating to that movement.

But the material didn’t stretch much, which would limit its use for soft robotics. Inspired by kirigami — a variation of origami that includes making cuts in a material — Truby designed and laser-cut rectangular strips of conductive silicone sheets into various patterns, such as rows of tiny holes or crisscrossing slices like a chain link fence.

That made them far more flexible, stretchable, “and beautiful to look at”, says Truby.

The researchers’ robotic trunk comprises three segments, each with four fluidic actuators (12 total) used to move the arm.

They fused one sensor over each segment, with each sensor covering and gathering data from one embedded actuator in the soft robot.

They used 'plasma bonding', a technique that energises a surface of a material to make it bond to another material.

It takes roughly a couple hours to shape dozens of sensors that can be bonded to the soft robots using a handheld plasma-bonding device.

'Learning' configurations


As hypothesised, the sensors did capture the trunk’s general movement. But they were really noisy. “Essentially, they’re non-ideal sensors in many ways,” says Truby.

“But that’s just a common fact of making sensors from soft conductive materials. Higher-performing and more reliable sensors require specialised tools that most robotics labs do not have.”

To estimate the soft robot’s configuration using only the sensors, the researchers built a deep neural network to do most of the heavy lifting, by sifting through the noise to capture meaningful feedback signals.

The researchers developed a new model to kinematically describe the soft robot’s shape that vastly reduces the number of variables needed for their model to process.

In experiments, the researchers had the trunk swing around and extend itself in random configurations over approximately an hour and a half. They used the traditional motion-capture system for ground truth data.

In training, the model analysed data from its sensors to predict a configuration, and compared its predictions to that ground truth data which was being collected simultaneously.

In doing so, the model 'learns' to map signal patterns from its sensors to real-world configurations. Results indicated, that for certain and steadier configurations, the robot’s estimated shape matched the ground truth.

Next, the researchers aim to explore new sensor designs for improved sensitivity and to develop new models and deep-learning methods to reduce the required training for every new soft robot. They also hope to refine the system to better capture the robot’s full dynamic motions.

Currently, the neural network and sensor skin are not sensitive to capture subtle motions or dynamic movements.

But, for now, this is an important first step for learning-based approaches to soft robotic control, Truby says: “Like our soft robots, living systems don’t have to be totally precise. Humans are not precise machines, compared to our rigid robotic counterparts, and we do just fine.”

'Sensorised' skin helps soft robots find their bearings

Researchers develop a more robust machine-vision architecture by studying how human vision responds to changing viewpoints of objects.

Suppose you look briefly from a few feet away at a person you have never met before. Step back a few paces and look again. Will you be able to recognise her face? “Yes, of course,” you probably are thinking.

If this is true, it would mean that our visual system, having seen a single image of an object such as a specific face, recognises it robustly despite changes to the object’s position and scale, for example.

Vanilla deep networks


On the other hand, we know that state-of-the-art classifiers, such as vanilla deep networks, will fail this simple test.

In order to recognise a specific face under a range of transformations, neural networks need to be trained with many examples of the face under the different conditions.

In other words, they can achieve invariance through memorisation, but cannot do it if only one image is available.

Thus, understanding how human vision can pull off this remarkable feat is relevant for engineers aiming to improve their existing classifiers.

It also is important for neuroscientists modelling the primate visual system with deep networks. In particular, it is possible that the invariance with one-shot learning exhibited by biological vision requires a rather different computational strategy than that of deep networks.

A paper by MIT PhD candidate in electrical engineering and computer science Yena Han and colleagues in 'Nature Scientific Reports' entitled 'Scale and translation-invariance for novel objects in human vision' discusses how they study this phenomenon more carefully to create novel biologically inspired networks.

Vast implications for engineering of vision systems


"Humans can learn from very few examples, unlike deep networks. This is a huge difference with vast implications for engineering of vision systems and for understanding how human vision really works," states co-author Tomaso Poggio — director of the Center for Brains, Minds and Machines (CBMM) and the Eugene McDermott Professor of Brain and Cognitive Sciences at MIT.

"A key reason for this difference is the relative invariance of the primate visual system to scale, shift, and other transformations.

"Strangely, this has been mostly neglected in the AI community, in part because the psychophysical data were so far less than clear-cut. Han's work has now established solid measurements of basic invariances of human vision.”

To differentiate invariance rising from intrinsic computation with that from experience and memorisation, the new study measured the range of invariance in one-shot learning.

A one-shot learning task was performed by presenting Korean letter stimuli to human subjects who were unfamiliar with the language.

These letters were initially presented a single time under one specific condition and tested at different scales or positions than the original condition.

The first experimental result is that — just as you guessed — humans showed significant scale-invariant recognition after only a single exposure to these novel objects. The second result is that the range of position-invariance is limited, depending on the size and placement of objects.

Next, Han and her colleagues performed a comparable experiment in deep neural networks designed to reproduce this human performance.

The results suggest that to explain invariant recognition of objects by humans, neural network models should explicitly incorporate built-in scale-invariance.

Limited position-invariance of human vision


In addition, limited position-invariance of human vision is better replicated in the network by having the model neurons’ receptive fields increase as they are further from the centre of the visual field.

This architecture is different from commonly used neural network models, where an image is processed under uniform resolution with the same shared filters.

“Our work provides a new understanding of the brain representation of objects under different viewpoints. It also has implications for AI, as the results provide new insights into what is a good architectural design for deep neural networks,” remarks Han, CBMM researcher and lead author of the study.

Bridging the gap between human and machine vision

Theme picker

Engineers Ireland
Engineers TV Live broadcast channel
View live broadcasts from Engineers Ireland