HIGHLIGHTS
SUMMARY
Interaction-based wrist contro is augmented with a form of active perception by a soft skin with 32 soft barometric sensors. The authors study the grasping performance of the hand in a selfresetting environment, which allows large-scale experimentation. A long short-term memory (LSTM) network is used to predict real-time failure and success from few trials, using exteroceptive and proprioceptive data from the soft, modular sensors. Real-time error predictions and a heuristic error recovery routine are implemented and compared to grasping with no feedback, resulting in an 144% improvement in success . . .
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