From Mystery to Mastery: Failure Diagnosis for Improving Manipulation Policies

2024-12-03

AI Summary

Robotic manipulation policies frequently suffer from unknown failures in real-world environments due to unforeseen environmental changes; existing heuristic diagnostic approaches suffer from low coverage, poor interpretability, and high operational cost. This paper introduces RoboMD, an automated failure diagnosis framework that pioneers a vision-language embedding–guided deep reinforcement learning exploration mechanism to probabilistically identify, quantify, and prioritize environment-induced failure modes. By integrating failure-aware representation learning with cross-task generalization design, RoboMD significantly improves detection rates for previously unseen failures (+32.7%) and enhances attribution interpretability across diverse manipulation tasks and policy architectures. It overcomes critical coverage gaps inherent in conventional methods and establishes a reusable, robustness-oriented diagnostic paradigm for systematic policy optimization.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

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.

Problem

• Automate failure diagnosis in robot manipulation
• Identify unknown environmental failure modes
• Improve robustness using deep reinforcement learning

Innovation

• deep reinforcement learning
• vision-language embedding
• systematic failure diagnosis