Model-Free Error Detection and Recovery for Robot Learning from Demonstration
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Learning from human demonstrations can facilitate automation but is risky because the execution of the learned policy might lead to collisions and other failures. Adding explicit constraints to avoid unsafe states is generally not possible when the state representations are complex. Furthermore, enforcing these constraints during execution of the learned policy can be challenging in environments where dynamics are difficult to model such as push mechanics in grasping. In this paper, we propose a two-phase method for generating robust policies from demonstrations in robotic manipulation tasks. In the first phase, we use support estimation of supervisor demonstrations and treat the support as implicit constraints on states in addition to learning a policy directly from the observed controls. We also propose a time-variant modification to the support estimation problem allowing for accurate estimation on sequential tasks. In the second phase, we use a switching policy to steer the robot from leaving safe regions of the state space during run time using the decision function of the estimated support. The policy switches between the robot's learned policy and a novel recovery policy depending on the distance to the boundary of the support. We present additional conditions, which linearly bound the difference in state at each time step by the magnitude of control, allowing us to prove that the robot will not violate the constraints using the recovery policy. A simulated pushing task suggests that support estimation and recovery control can reduce collisions by 83 da Vinci Surgical Robot, recovery control reduced collisions by 84
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