  Robots deployed in the real world over extended periods of time need to
reason about unexpected failures, learn to predict them, and to proactively
take actions to avoid future failures. Existing approaches for competence-aware
planning are either model-based, requiring explicit enumeration of known
failure modes, or purely statistical, using state- and location-specific
failure statistics to infer competence. We instead propose a structured
model-free approach to competence-aware planning by reasoning about plan
execution failures due to errors in perception, without requiring a priori
enumeration of failure sources or requiring location-specific failure
statistics. We introduce competence-aware path planning via introspective
perception (CPIP), a Bayesian framework to iteratively learn and exploit
task-level competence in novel deployment environments. CPIP factorizes the
competence-aware planning problem into two components. First, perception errors
are learned in a model-free and location-agnostic setting via introspective
perception prior to deployment in novel environments. Second, during actual
deployments, the prediction of task-level failures is learned in a
context-aware setting. Experiments in a simulation show that the proposed CPIP
approach outperforms the frequentist baseline in multiple mobile robot tasks,
and is further validated via real robot experiments in an environment with
perceptually challenging obstacles and terrain.
