Researchers have created a system that lets robots effectively use grasped tools with
the correct amount of force.
Tool use has long been a hallmark of human intelligence, as well as a practical problem
to solve for a vast array of robotic applications.
But machines are still wonky at exerting just the right amount of force to control tools
that aren’t rigidly attached to their hands.
To manipulate said tools more robustly, researchers from MIT’s Computer Science and Artificial
Intelligence Laboratory (CSAIL), in collaboration with the Toyota Research Institute (TRI),
have designed a system that can grasp tools and apply the appropriate amount of force
for a given task, like squeegeeing up liquid or writing out a word with a pen.
The system, dubbed Series Elastic End Effectors, or SEED, uses soft bubble grippers and embedded
cameras to map how the grippers deform over a six-dimensional space (think of an airbag
inflating and deflating) and apply force to a tool.
Using six degrees of freedom, the object can be moved left and right, up or down, back
and forth, roll, pitch, and yaw.
The closed-loop controller — a self-regulating system that maintains a desired state without
human interaction — uses SEED and visuotactile feedback to adjust the position of the robot
arm in order to apply the desired force.
This could be useful, for example, for someone using tools when there’s uncertainty in
the height of a table, since a pre-programmed trajectory might miss the table completely.
Rigid-bodied robots and their counterparts can only take us so far; softness and compliance
affords the luxury and ability to deform, to sense the interaction between the tool
and the hand.
With SEED, every execution the robot senses is a recent 3D image from the grippers, thereby
tracking in real-time how the grippers are changing shape around an object.
These images are used to reconstruct the position of the tool, and the robot uses a learned
model to map the position of the tool to the measured force.
The learned model is obtained using the robot's previous experience, where it disturbs a force
torque sensor to figure out how stiff the bubble grippers are.
Now, once the robot has sensed the force, it will compare that with the force that the
user commands.
It would then move in the direction to increase the force, all done over 6D space.
During the “squeegee task,” SEED was provided the right amount of force to wipe up some
liquid on a plane, where baseline methods struggled to get the right sweep.
When asked to put paper to pen, the bot effectively wrote out “MIT,” and it was also able
to apply the right amount of force to drive a screw.
While SEED was aware of the fact that it needs to command the force or torque for a given
task, if grasped too hard, the item would inevitably slip, so there’s an upper limit
on that exerted hardness.
Also, if you’re a stiff robot, you can simulate softer systems than your natural mechanical
stiffness — but not the other way around.
Currently, the system assumes a very specific geometry for the tools: it has to be cylindrical,
and there are still many limitations on how it may generalize when it meets new types
of shapes.
Forthcoming work might involve generalizing the framework to different shapes so it can
handle arbitrary tools in the wild.
The researchers will present the work at the IEEE/RSJ International Conference on Intelligent
Robots and Systems conference in October.