  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.
