  Many robot manipulation skills can be represented with deterministic
characteristics and there exist efficient techniques for learning parameterized
motor plans for those skills. However, one of the active research challenge
still remains to sustain manipulation capabilities in situation of a mechanical
failure. Ideally, like biological creatures, a robotic agent should be able to
reconfigure its control policy by adapting to dynamic adversaries. In this
paper, we propose a method that allows an agent to survive in a situation of
mechanical loss, and adaptively learn manipulation with compromised degrees of
freedom -- we call our method Survivable Robotic Learning (SRL). Our key idea
is to leverage Bayesian policy gradient by encoding knowledge bias in posterior
estimation, which in turn alleviates future policy search explorations, in
terms of sample efficiency and when compared to random exploration based policy
search methods. SRL represents policy priors as Gaussian process, which allows
tractable computation of approximate posterior (when true gradient is
intractable), by incorporating guided bias as proxy from prior replays. We
evaluate our proposed method against off-the-shelf model free learning
algorithm (DDPG), testing on a hexapod robot platform which encounters
incremental failure emulation, and our experiments show that our method
improves largely in terms of sample requirement and quantitative success ratio
in all failure modes. A demonstration video of our experiments can be viewed
at: https://sites.google.com/view/survivalrl
