Robotic hand can identify objects with just one grasp
The three-fingered robotic gripper can 'feel' with great sensitivity
along the full length of each finger -- not just at the tips

Date:
April 3, 2023
Source:
Massachusetts Institute of Technology
Summary:
Newly created soft-rigid robotic fingers incorporate powerful
sensors along their entire length, enabling them to produce a
robotic hand that could accurately identify objects after only
one grasp.


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FULL STORY ========================================================================== Inspired by the human finger, MIT researchers have developed a robotic
hand that uses high-resolution touch sensing to accurately identify an
object after grasping it just one time.


==========================================================================
Many robotic hands pack all their powerful sensors into the fingertips, so
an object must be in full contact with those fingertips to be identified,
which can take multiple grasps. Other designs use lower-resolution sensors spread along the entire finger, but these don't capture as much detail,
so multiple regrasps are often required.

Instead, the MIT team built a robotic finger with a rigid skeleton
encased in a soft outer layer that has multiple high-resolution sensors incorporated under its transparent "skin." The sensors, which use a
camera and LEDs to gather visual information about an object's shape,
provide continuous sensing along the finger's entire length. Each finger captures rich data on many parts of an object simultaneously.

Using this design, the researchers built a three-fingered robotic hand
that could identify objects after only one grasp, with about 85 percent accuracy.

The rigid skeleton makes the fingers strong enough to pick up a heavy
item, such as a drill, while the soft skin enables them to securely grasp
a pliable item, like an empty plastic water bottle, without crushing it.

These soft-rigid fingers could be especially useful in an at-home-care
robot designed to interact with an elderly individual. The robot could
lift a heavy item off a shelf with the same hand it uses to help the
individual take a bath.

"Having both soft and rigid elements is very important in any hand,
but so is being able to perform great sensing over a really large area, especially if we want to consider doing very complicated manipulation
tasks like what our own hands can do. Our goal with this work was to
combine all the things that make our human hands so good into a robotic
finger that can do tasks other robotic fingers can't currently do,"
says mechanical engineering graduate student Sandra Liu, co-lead author
of a research paper on the robotic finger.

Liu wrote the paper with co-lead author and mechanical engineering undergraduate student Leonardo Zamora Yan~ez and her advisor, Edward
Adelson, the John and Dorothy Wilson Professor of Vision Science in the Department of Brain and Cognitive Sciences and a member of the Computer
Science and Artificial Intelligence Laboratory (CSAIL). The research
will be presented at the RoboSoft Conference.

A human-inspired finger The robotic finger is comprised of a rigid,
3D-printed endoskeleton that is placed in a mold and encased in a
transparent silicone "skin." Making the finger in a mold removes the
need for fasteners or adhesives to hold the silicone in place.

The researchers designed the mold with a curved shape so the robotic
fingers are slightly curved when at rest, just like human fingers.

"Silicone will wrinkle when it bends, so we thought that if we have the
finger molded in this curved position, when you curve it more to grasp
an object, you won't induce as many wrinkles. Wrinkles are good in some
ways -- they can help the finger slide along surfaces very smoothly and
easily -- but we didn't want wrinkles that we couldn't control," Liu says.

The endoskeleton of each finger contains a pair of detailed touch sensors, known as GelSight sensors, embedded into the top and middle sections, underneath the transparent skin. The sensors are placed so the range
of the cameras overlaps slightly, giving the finger continuous sensing
along its entire length.

The GelSight sensor, based on technology pioneered in the Adelson group,
is composed of a camera and three colored LEDs. When the finger grasps
an object, the camera captures images as the colored LEDs illuminate
the skin from the inside.

Using the illuminated contours that appear in the soft skin, an algorithm performs backward calculations to map the contours on the grasped object's surface. The researchers trained a machine-learning model to identify
objects using raw camera image data.

As they fine-tuned the finger fabrication process, the researchers ran
into several obstacles.

First, silicone has a tendency to peel off surfaces over time. Liu and
her collaborators found they could limit this peeling by adding small
curves along the hinges between the joints in the endoskeleton.

When the finger bends, the bending of the silicone is distributed along
the tiny curves, which reduces stress and prevents peeling. They also
added creases to the joints so the silicone is not squashed as much when
the finger bends.

While troubleshooting their design, the researchers realized wrinkles
in the silicone prevent the skin from ripping.

"The usefulness of the wrinkles was an accidental discovery on our
part. When we synthesized them on the surface, we found that they actually
made the finger more durable than we expected," she says.

Getting a good grasp Once they had perfected the design, the researchers
built a robotic hand using two fingers arranged in a Y pattern with a
third finger as an opposing thumb.

The hand captures six images when it grasps an object (two from each
finger) and sends those images to a machine-learning algorithm which
uses them as inputs to identify the object.

Because the hand has tactile sensing covering all of its fingers, it
can gather rich tactile data from a single grasp.

"Although we have a lot of sensing in the fingers, maybe adding a palm
with sensing would help it make tactile distinctions even better,"
Liu says.

In the future, the researchers also want to improve the hardware to
reduce the amount of wear and tear in the silicone over time and add
more actuation to the thumb so it can perform a wider variety of tasks.

This work was supported, in part, by the Toyota Research Institute,
the Office of Naval Research, and the SINTEF BIFROST project.

* RELATED_TOPICS
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========================================================================== Story Source: Materials provided by
Massachusetts_Institute_of_Technology. Original written by Adam
Zewe. Note: Content may be edited for style and length.


========================================================================== Related Multimedia:
* Soft-rigid_robotic_finger ========================================================================== Journal Reference:
1. Sandra Q. Liu, Leonardo Zamora Yan~ez, Edward H. Adelson. GelSight
EndoFlex: A Soft Endoskeleton Hand with Continuous
High-Resolution Tactile Sensing. Submitted to arXiv, 2023 DOI:
10.48550/arXiv.2303.17935 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/04/230403133515.htm

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