  Self-assessment rules play an essential role in safe and effective real-world
robotic applications, which verify the feasibility of the selected action
before actual execution. But how to utilize the self-assessment results to
re-choose actions remains a challenge. Previous methods eliminate the selected
action evaluated as failed by the self-assessment rules, and re-choose one with
the next-highest affordance~(i.e. process-of-elimination strategy [1]), which
ignores the dependency between the self-assessment results and the remaining
untried actions. However, this dependency is important since the previous
failures might help trim the remaining over-estimated actions. In this paper,
we set to investigate this dependency by learning a failure-aware policy. We
propose two architectures for the failure-aware policy by representing the
self-assessment results of previous failures as the variable state, and
leveraging recurrent neural networks to implicitly memorize the previous
failures. Experiments conducted on three tasks demonstrate that our method can
achieve better performances with higher task success rates by less trials.
Moreover, when the actions are correlated, learning a failure-aware policy can
achieve better performance than the process-of-elimination strategy.
