If you build machines, you probably work with actuators and positioning stages every day. But do you truly get the best performance or lowest cost of ownership from these motion devices? The answer may not be what you expect.
From Vikash Kumar at University of Washington:
Dexterous hand manipulation is one of the most complex types of biological movement, and has proven very difficult to replicate in robots. The usual approaches to robotic control - following pre-defined trajectories or planning online with reduced models - are both inapplicable. Dexterous manipulation is so sensitive to small variations in contact force and object location that it seems to require online planning without any simplifications. Here we demonstrate for the first time online planning (or model-predictive control) with a full physics model of a humanoid hand, with 28 degrees of freedom and 48 pneumatic actuators. We augment the actuation space with motor synergies which speed up optimization without removing flexibility. Most of our results are in simulation, showing nonprehensile object manipulation as well as typing. In both cases the input to the system is a high level task description, while all details of the hand movement emerge online from fully automated numerical optimization. We also show preliminary results on a hardware platform we have developed "ADROIT" - a ShadowHand skeleton equipped with faster and more compliant actuation... (website)
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