Smart algorithm automatically adjusts exoskeletons for best walking performance

Walk this way: Metabolic feedback and optimization algorithm automatically tweaks exoskeleton for optimal performance. (credit: Kirby Witte, Katie Poggensee, Pieter Fiers, Patrick Franks & Steve Collins)

Researchers at the College of Engineering at Carnegie Mellon University (CMU) have developed a new automated feedback system for personalizing exoskeletons to achieve optimal performance.

Exoskeletons can be used to augment human abilities. For example, they can provide more endurance while walking, help lift a heavy load, improve athletic performance, and help a stroke patient walk again.

But current one-size-fits-all exoskeleton devices, despite their potential, “have not improved walking performance as much as we think they should,” said Steven Collins, a professor of Mechanical Engineering and senior author of a paper published published Friday June 23, 2017 in Science.

The problem: An exoskeleton needs to be adjusted (and re-adjusted) to work effectively for each user — currently, a time-consuming, iffy manual process.

So the CMU engineers developed a more effective “human-in-the-loop optimization” technique that measures the amount of energy the walker expends by monitoring their breathing* — automatically adjusting the exoskeleton’s ankle dynamics to minimize required human energy expenditure.**

Using real-time metabolic cost estimation for each individual, the CMU software algorithm, combined with versatile emulator hardware, optimized the exoskeleton torque pattern for one ankle while walking, running, and carrying a load on a treadmill. The algorithm automatically made optimized adjustments for each pattern, based on measurements of a person’s energy use for 32 different walking patterns over the course of an hour. (credit: Juanjuan Zhang et al./Science, adapted by KurzweilAI)

In a lab study with 11 healthy volunteers, the new technique resulted in an average reduction in effort of 24% compared to participants walking with the exoskeleton powered off. The technique yielded higher user benefits than in any exoskeleton study to date, including devices acting at all joints on both legs, according to the researchers.

* “In daily life, a proxy measure such as heart rate or muscle activity could be used for optimization, providing noisier but more abundant performance data.” — Juanjuan Zhang et al./Science

** Ankle torque in the lab study was determined by four parameters: peak torque, timing of peak torque, and rise and fall times. This method was chosen to allow comparisons to a prior study that used the same hardware.

Science/AAAS | Personalized Exoskeletons Are Taking Support One Step Farther

Abstract of Human-in-the-loop optimization of exoskeleton assistance during walking

Exoskeletons and active prostheses promise to enhance human mobility, but few have succeeded. Optimizing device characteristics on the basis of measured human performance could lead to improved designs. We have developed a method for identifying the exoskeleton assistance that minimizes human energy cost during walking. Optimized torque patterns from an exoskeleton worn on one ankle reduced metabolic energy consumption by 24.2 ± 7.4% compared to no torque. The approach was effective with exoskeletons worn on one or both ankles, during a variety of walking conditions, during running, and when optimizing muscle activity. Finding a good generic assistance pattern, customizing it to individual needs, and helping users learn to take advantage of the device all contributed to improved economy. Optimization methods with these features can substantially improve performance.

Tactile sensor lets robots gauge objects’ hardness and manipulate small tools

A GelSight sensor attached to a robot’s gripper enables the robot to determine precisely where it has grasped a small screwdriver, removing it from and inserting it back into a slot, even when the gripper screens the screwdriver from the robot’s camera. (credit: Robot Locomotion Group at MIT)

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have added sensors to grippers on robot arms to give robots greater sensitivity and dexterity. The sensor can judge the hardness of surfaces it touches, enabling a robot to manipulate smaller objects than was previously possible.

The “GelSight” sensor consists of a block of transparent soft rubber — the “gel” of its name — with one face coated with metallic paint. It is mounted on one side of a robotic gripper. When the paint-coated face is pressed against an object, the face conforms to the object’s shape and the metallic paint makes the object’s surface reflective. Mounted on the sensor opposite the paint-coated face of the rubber block are three colored lights at different angles and a single camera.

Humans gauge hardness by the degree to which the contact area between the object and our fingers changes as we press on it. Softer objects tend to flatten more, increasing the contact area. The MIT researchers used the same approach.

A GelSight sensor, pressed against each object manually, recorded how the contact pattern changed over time, essentially producing a short movie for each object. A neural network was then used to look for correlations between changes in contact patterns and hardness measurements. The resulting system takes frames of video as inputs and produces hardness scores with very high accuracy.

The researchers also designed control algorithms that use a computer vision system to guide the robot’s gripper toward a tool and then turn location estimation over to a GelSight sensor once the robot has the tool in hand.

“I think that the GelSight technology, as well as other high-bandwidth tactile sensors, will make a big impact in robotics,” says Sergey Levine, an assistant professor of electrical engineering and computer science at the University of California at Berkeley. “For humans, our sense of touch is one of the key enabling factors for our amazing manual dexterity. Current robots lack this type of dexterity and are limited in their ability to react to surface features when manipulating objects. If you imagine fumbling for a light switch in the dark, extracting an object from your pocket, or any of the other numerous things that you can do without even thinking — these all rely on touch sensing.”

The researchers presented their work in two papers at the International Conference on Robotics and Automation.

Wenzhen Yuan | Measuring hardness of fruits with GelSight sensor

Abstract of Tracking Objects with Point Clouds from Vision and Touch

We present an object-tracking framework that fuses point cloud information from an RGB-D camera with tactile information from a GelSight contact sensor. GelSight can be treated as a source of dense local geometric information, which we incorporate directly into a conventional point-cloud-based articulated object tracker based on signed-distance functions. Our implementation runs at 12 Hz using an online depth reconstruction algorithm for GelSight and a modified secondorder update for the tracking algorithm. We present data from hardware experiments demonstrating that the addition of contact-based geometric information significantly improves the pose accuracy during contact, and provides robustness to occlusions of small objects by the robot’s end effector.