PREFAIL: Identifying Precursors to Failures in Robotic Lift-and-Place Tasks to Improve Task Execution Performance

2026-07-18

AI Summary

This work addresses the challenge of object slippage during high-speed non-grasping manipulation, where frictional support often leads to task failure and existing methods struggle to reliably predict actionable precursors to such failures. The authors propose a strategy-agnostic and dynamically robust precursor prediction approach that identifies failure indicators by analyzing relative motion between the object and the carrier. They introduce and precisely define the "Latest Intervention Time" (LIT)—the final moment at which intervention can prevent failure—and curate the first dataset annotated with LIT to enable operability-aware evaluation. Experimental validation in both simulation and real-world settings demonstrates that the proposed method significantly improves the accuracy and timeliness of failure precursor prediction, thereby enabling effective timely interventions and substantially increasing task success rates.

Abstract

Non-prehensile manipulation enables flexible material handling with part carriers, but friction-based support makes high-speed motions failure-prone, while slower operation increases cycle time. Proactive failure prediction is therefore essential for efficient and reliable performance, yet existing approaches remain limited by key constraints, including sensitivity to dynamic actions and high dependence on known policy structures. Furthermore, existing methods and datasets lack a precise characterization of the latest intervention time, leaving it unclear whether a detected failure can still be prevented through timely intervention. In this paper, we investigate lift-and-place tasks for non-prehensile material handling manipulation and propose a more effective approach to predicting precursors to failures (PREFAIL) by analyzing the relative motion of target objects with respect to the carrier. We further introduce a dataset that precisely identifies the latest intervention time for risky manipulations, enabling rigorous evaluation of whether a failure prediction is actionable. We validate our approach on both simulation and real-world datasets. Our experimental results demonstrate that PREFAIL substantially improves both the accuracy and timeliness of responses to failure precursors.

Problem

• failure prediction
• non-prehensile manipulation
• lift-and-place tasks
• intervention time
• robotic material handling

Innovation

• failure prediction
• non-prehensile manipulation
• relative motion analysis
• latest intervention time
• robotic lift-and-place