Tactile Learning

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We introduce velcro peeling as a representative application for robotic manipulating of non-rigid objects in complex environments.

We design strategies for peeling velcro strips placed on various surfaces such as planes and cylinders. Specifically, we formulate the problem as a Partially Observable Markov Decision Process and solve it using a multi-step deep recurrent network for the cases of tactile-only and visual feedback. We present a custom simulation setup and a real experiment setup, which is used to validate the strategies and compare them against benchmark strategies, including the ideal, fully-observable case. Our results show that tactile input can be used to overcome geometric uncertainties present in the environment when designing robust robotic manipulation controllers.

Brief introduction for the project

Multi-step DRQN training result examples