Dexterous robotic hands manipulate thousands of objects with ease

Researchers at MIT have developed a model-free framework that reorients over 2000 diverse objects with a robot hand facing both upward and downward, in a step towards more human-like manipulation.

At just one year old, a baby is more dexterous than a robot. Sure, machines can do more than just pick up and put down objects, but we’re not quite there as far as replicating a natural pull toward exploratory or sophisticated dexterous manipulation goes.

Scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), in the ever-present quest to get machines to replicate human abilities, created a system that can reorient over 2000 different objects, with the robotic hand facing both upwards and downwards. This ability to manipulate anything from a cup to a tuna can to a snack box could help the hand quickly pick and place objects in specific ways and locations — and even generalise to unseen objects.

This deft ‘handiwork’ — which is usually limited to single tasks and upright positions — could be an asset in speeding up logistics and manufacturing, helping with common demands such as packing objects into slots for kitting or dexterously manipulating a wider range of tools. The team used a simulated, anthropomorphic hand with 24 degrees of freedom, and showed evidence that the system could be transferred to a real robotic system in the future.

“In industry, a parallel jaw gripper is most commonly used, partially due to its simplicity in control, but it’s physically unable to handle many tools we see in daily life,” said MIT CSAIL PhD student Tao Chen, a member of the MIT Improbable AI Lab and the lead researcher on the project. “Even using a plier is difficult because it can’t dexterously move one handle back and forth. Our system will allow a multi-fingered hand to dexterously manipulate such tools, which opens up a new area for robotics applications.”

This type of ‘in-hand’ object reorientation has been a challenging problem in robotics, due to the large number of motors to be controlled and the frequent change in contact state between the fingers and the objects. And with over 2000 objects, the model had a lot to learn.

The problem becomes even more tricky when the hand is facing downwards. Not only does the robot need to manipulate the object, but also circumvent gravity so it doesn’t fall down.

The team found that a simple approach could solve complex problems. They used a model-free reinforcement learning algorithm (meaning the system has to figure out value functions from interactions with the environment) with deep learning, and something called a ‘teacher–student’ training method.

Image courtesy of MIT CSAIL.

For this to work, the ‘teacher’ network is trained on information about the object and robot that’s easily available in simulation but not in the real world, such as the location of fingertips or object velocity. To ensure that the robots can work outside of the simulation, the knowledge of the teacher is distilled into observations that can be acquired in the real world, such as depth images captured by cameras, object pose and the robot’s joint positions. They also used a ‘gravity curriculum’, where the robot first learns the skill in a zero-gravity environment and then slowly adapts the controller to the normal gravity condition, which, when taking things at this pace, really improved the overall performance.

While seemingly counterintuitive, a single controller (known as the brain of the robot) could reorient a large number of objects it had never seen before, and with no knowledge of shape.

“We initially thought that visual perception algorithms for inferring shape while the robot manipulates the object was going to be the primary challenge,” said MIT Professor Pulkit Agrawal, an author on the research paper. “To the contrary, our results show that one can learn robust control strategies that are shape-agnostic. This suggests that visual perception may be far less important for manipulation than what we are used to thinking, and simpler perceptual processing strategies might suffice.”

Many small, circular shaped objects (apples, tennis balls, marbles) had close to 100% success rates when reoriented with the hand facing up and down, with the lowest success rates, unsurprisingly, for more complex objects, like a spoon, a screwdriver or scissors, being closer to 30%.

Beyond bringing the system out into the wild, since success rates varied with object shape, in the future, the team notes that training the model based on object shapes could improve performance.

Top image credit: ©stock.adobe.com/au/LIGHTFIELD STUDIOS