AED: Adaptable Error Detection for Few-shot Imitation Policy

We study how to report few-shot imitation (FSI) policies' behavior errors in novel environments, a novel task named adaptable error detection (AED). The potential to cause serious damage to surrounding areas limits the application of FSI policies in real-world scenarios. Thus, a robust system is necessary to notify operators when FSI policies are inconsistent with the intent of demonstrations. We develop a cross-domain benchmark for the challenging AED task, consisting of 329 base and 158 novel environments. This task introduces three challenges, including (1) detecting behavior errors in novel environments, (2) behavior errors occurring without revealing notable changes, and (3) lacking complete temporal information of the rollout due to the necessity of online detection. To address these challenges, we propose Pattern Observer (PrObe) to parse discernible patterns in the policy feature representations of normal or error states, whose effectiveness is verified in the proposed benchmark. Through our comprehensive evaluation, PrObe consistently surpasses strong baselines and demonstrates a robust capability to identify errors arising from a wide range of FSI policies. Moreover, we conduct comprehensive ablations and experiments (error correction, demonstration quality, etc.) to validate the practicality of our proposed task and methodology.