This is a short piece I wrote for a workshop on what are good things to work on in robotics research.

One measure of success of robots is how many of them get deployed doing real work in the real world. One way to get more robots deployed is to reduce the friction that comes up during typical deployments. For intelligent robots in factories there are many sources of friction, some sociological, some financial, some concerning takt time, some concerning PLCs and other automation, but perhaps the most friction that can be attributed to a lack of relevant research results is the problem of getting a gripper suitable for a particular task.

Today in factories the most commonly used grippers are either a set of custom configured suction cups to pick up a very particular object, or one of a myriad of parallel jaw grippers varying over a large number of parameters, and custom fingers, again carefully selected for a particular object. In both cases just one grasp is used for that particular object. Getting the right gripper for initial deployment can be a weeks long source of friction, and then changing the gripper when new objects are to be handled is another source of friction. Furthermore, grip failure can be a major source of run time errors.

Human hands just work. Give them an object from a very wide class of objects and they grip that object, usually with a wide variety of possible grips. They sense when the grip is failing and adjust. They work reliably and quickly.

Building more general hands for robots that require very little customization, that can dynamically grasp millions of different sized and shaped objects, that can do so quickly, that have a long lifetime over millions of cycles, and that just work would have significant impact on deployment of robots in factories, in fulfillment centers, and in homes.

Things like SLAM took many hundreds of researchers working for many years with an ultimately well defined problem (that definition took a few years to appear), and with access to low cost robots that could be used to produce dynamic data sets in many different environments.

Right now it is hard to define a mathematical criterion for a good robot hand, i.e., we can see nothing, and may never see anything, of comparable clarity as we had for SLAM.

My strawman is that we will need concurrent progress in at least five areas, each feeding off the other, in order to come up with truly useful and general robot hands:

– new (low cost) mechanisms for both kinematics and force control
– materials to act as a skin (grasp properties and longevity)
– long life sensors that can be embedded in the skin and mechanism
– algorithms to dynamically adjust grasps based on sensing
– learning mechanisms on visual/3D data to inform hands for pregrasp

I think progress on one of these alone is hard to get adopted by research groups working on others. The constraints between them are not well understood and need to be challenged and adapted to by all the researchers. This is a tall order. This is why grippers on factory robots today look just like they did forty years ago.