Survivable Hyper-Redundant Robotic Arm with Bayesian Policy Morphing
This addresses the survivability of robotic arms in unpredictable environments, representing an incremental improvement over existing actor-critic methods.
The paper tackles the problem of robotic manipulators adapting to random mechanical failures by introducing a Bayesian reinforcement learning framework called Bayesian Policy Morphing (BPM), which enables an 8-DOF robotic arm to maintain functionality and accurately grasp targets even with damaged joints.
In this paper we present a Bayesian reinforcement learning framework that allows robotic manipulators to adaptively recover from random mechanical failures autonomously, hence being survivable. To this end, we formulate the framework of Bayesian Policy Morphing (BPM) that enables a robot agent to self-modify its learned policy after the diminution of its maneuvering dimensionality. We build upon existing actor-critic framework, and extend it to perform policy gradient updates as posterior learning, taking past policy updates as prior distributions. We show that policy search, in the direction biased by prior experience, significantly improves learning efficiency in terms of sampling requirements. We demonstrate our results on an 8-DOF robotic arm with our algorithm of BPM, while intentionally disabling random joints with different damage types like unresponsive joints, constant offset errors and angular imprecision. Our results have shown that, even with physical damages, the robotic arm can still successfully maintain its functionality to accurately locate and grasp a given target object.