Problem
Research questions and friction points this paper is trying to address.
Failure Detection
Robotic Manipulation
Out-of-Distribution
RAFAIL: Relationship-Aware Failure Detection for Robotic Manipulation
🤖 AI Summary
RAFAIL通过检测任务相关实体间的关系异常来识别机器人操作中的执行失败，减少了对无关场景变化的敏感性，无需故障数据。
📝 Abstract
Detecting failures during execution is essential for reliable robotic manipulation. Vision-language models (VLMs) can assess task outcomes semantically but add runtime computation, whereas out-of-distribution (OOD) detectors may respond to harmless scene variations rather than failure-relevant deviations. We introduce RAFAIL, a framework for detecting execution failures during robotic manipulation. RAFAIL identifies failures by detecting anomalies in task-relevant relationships between entities, such as a gripper and an object or an object and its target. By focusing OOD detection on relevant parts of the observation, RAFAIL reduces sensitivity to task-irrelevant scene variation. Offline, a VLM annotates successful demonstrations with task progress and relationship importance, which are used to learn point-cloud-based relationship representations without relying on policy-internal features. At runtime, relationship-specific OOD detectors evaluate these representations while relationship importance and task progress are predicted without VLM inference. RAFAIL requires no failure data and achieves 73.4% balanced accuracy across three real-world robotic manipulation tasks, outperforming the strongest evaluated OOD- and uncertainty-based baselines.
Innovation
Methods, ideas, or system contributions that make the work stand out.
relationship-aware
failure detection
out-of-distribution
point-cloud-based
visual-language model
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