From Mystery to Mastery: Failure Diagnosis for Improving Manipulation Policies
This addresses the challenge of improving robustness in robot manipulation for real-world deployment, though it is incremental as it builds on existing methods for failure diagnosis.
The paper tackles the problem of unknown failures in robot manipulation policies by introducing RoboMD, a framework that automatically identifies failure modes using deep reinforcement learning and a vision-language embedding, demonstrating effectiveness across various tasks and algorithms.
Robot manipulation policies often fail for unknown reasons, posing significant challenges for real-world deployment. Researchers and engineers typically address these failures using heuristic approaches, which are not only labor-intensive and costly but also prone to overlooking critical failure modes (FMs). This paper introduces Robot Manipulation Diagnosis (RoboMD), a systematic framework designed to automatically identify FMs arising from unanticipated changes in the environment. Considering the vast space of potential FMs in a pre-trained manipulation policy, we leverage deep reinforcement learning (deep RL) to explore and uncover these FMs using a specially trained vision-language embedding that encodes a notion of failures. This approach enables users to probabilistically quantify and rank failures in previously unseen environmental conditions. Through extensive experiments across various manipulation tasks and algorithms, we demonstrate RoboMD's effectiveness in diagnosing unknown failures in unstructured environments, providing a systematic pathway to improve the robustness of manipulation policies.