Rewind-IL: Online Failure Detection and State Respawning for Imitation Learning

• URL: http://arxiv.org/abs/2604.16683v1
• Date: Fri, 17 Apr 2026 20:41:14 GMT
• Title: Rewind-IL: Online Failure Detection and State Respawning for Imitation Learning
• Abstract summary: We present Rewind-IL, a training-free online safeguard framework for generative action-chunked imitation policies.<n> Rewind-IL combines a zero-shot failure detector based on Temporal Inter-chunk Discrepancy Estimate (TIDE), with split conformal prediction, with a state-respawning mechanism.<n>Online, Rewind-IL monitors self-consistency in overlapping action chunks, tracks similarity to the checkpoint library, and, upon failure, rewinds execution to the latest verified safe state.
• Score: 7.445072780282545
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Imitation learning has enabled robots to acquire complex visuomotor manipulation skills from demonstrations, but deployment failures remain a major obstacle, especially for long-horizon action-chunked policies. Once execution drifts off the demonstration manifold, these policies often continue producing locally plausible actions without recovering from the failure. Existing runtime monitors either require failure data, over-trigger under benign feature drift, or stop at failure detection without providing a recovery mechanism. We present Rewind-IL, a training-free online safeguard framework for generative action-chunked imitation policies. Rewind-IL combines a zero-shot failure detector based on Temporal Inter-chunk Discrepancy Estimate (TIDE), calibrated with split conformal prediction, with a state-respawning mechanism that returns the robot to a semantically verified safe intermediate state. Offline, a vision-language model identifies recovery checkpoints in demonstrations, and the frozen policy encoder is used to construct a compact checkpoint feature database. Online, Rewind-IL monitors self-consistency in overlapping action chunks, tracks similarity to the checkpoint library, and, upon failure, rewinds execution to the latest verified safe state before restarting inference from a clean policy state. Experiments on real-world and simulated long-horizon manipulation tasks, including transfer to flow-matching action-chunked policies, demonstrate that policy-internal consistency coupled with semantically grounded respawning offers a practical route to improved reliability in imitation learning. Supplemental materials are available at https://sjay05.github.io/rewind-il

Related papers

• FLARE: A Failure-Aware Framework for Autonomous Correction and Recovery in Visual-Language Robotic Manipulation [43.89036551517797]
  Vision-Language-Action Models (VLAs) have demonstrated significant promise in generalizing to complex, long-horizon robotic manipulation tasks.<n>Their performance remains brittle, as they are typically trained on trajectory-monotonic, failure-free demonstrations.<n>We propose FLARE, a novel framework that endows VLAs with robust error recovery capabilities through a Retry" and Reset" paradigm.
  arXiv  Detail & Related papers  (2026-08-27T05:58:06Z)
• ScrambleToolBench: Agents Search Exhaustively Even When Their Own Map Points to the Next Step [74.83535434786145]
  We introduce ScrambleToolBench, an interactive terminal benchmark designed to isolate behavioral reasoning.<n>The benchmark requires agents to uncover hidden tool behaviors entirely through trial-and-error interaction.<n>Our evaluation of state-of-the-art language models reveals that successful initial discovery does not translate into robust adaptation.
  arXiv  Detail & Related papers  (2026-08-03T15:07:46Z)
• FutureRTC: Real-Time Robot Execution with Anticipatory-Conditioned Action Chunking [51.97839311685636]
  FutureRTC is a plug-and-play adaptation framework that predicts execution-time observations and states for asynchronous VLA control without modifying the underlying policy.<n>We show that FutureRTC achieves robustness to inference delays, resulting in smoother trajectories, faster execution, and consistently higher task success rates.
  arXiv  Detail & Related papers  (2026-07-27T05:10:16Z)
• Operational Hallucination and Safety Drift in AI Agents [0.3823356975862005]
  Large language models (LLMs) serving as planners in tool-using autonomous agents introduce dynamic reliability risks in multi-turn execution.<n>This paper empirically characterizes two observed failure modes across multiple state-of-the-art LLMs: Safety Drift and Operational Hallucination.<n>We propose an Action-Aware Supervision Layer - a lightweight, plug-and-play architectural blueprint incorporating intent-action consistency checks, state tracking, and forced termination primitives.
  arXiv  Detail & Related papers  (2026-07-20T17:01:50Z)
• PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution [49.776611937968]
  We present PhyAgentOS, a foundation delivering scheduling, verification, memory, benchmarking, and safety as system-level services.<n>Its Session-Centered treats a session, not an action, as the minimum unit of scheduling, compatibility, supervised execution, evidence collection, and acceptance.<n>SessionVerifier distinguishes execution termination from semantic task completion via evidence-grounded verdicts of success, failure, or replan.<n> Benchmarking reuses the deployment session and verification path, so results trace to real execution.
  arXiv  Detail & Related papers  (2026-07-18T04:46:53Z)
• A Symbolic Neural CPU for Quantization-Simulated Writeback and Interpretable Program Execution [0.0]
  We introduce a trace-supervised symbolic neural CPU that combines recurrent control, an explicit operation router and trajectory supervision.<n>The model exposes the selected operation, source and destination registers, register trajectory, memory signals and writeback semantics.<n>Together, these results establish a trace-verifiable framework for interpretable, low-precision and controllable neural execution.
  arXiv  Detail & Related papers  (2026-07-10T22:55:23Z)
• Foresight: Failure Detection for Long-Horizon Robotic Manipulation with Action-Conditioned World Model Latents [58.49879338545782]
  Long-horizon tasks are common in real-world robotic deployments, yet failure detection for such tasks remains underexplored.<n>We present Foresight, a failure detection framework that monitors manipulation trajectories using latent representations from an action-conditioned world model.<n>Our results suggest that action-conditioned world-model embeddings provide a scalable representation for reliable failure monitoring in long-horizon manipulation.
  arXiv  Detail & Related papers  (2026-06-22T09:32:28Z)
• PATCH: Action-Chunk-Conditioned Latent Patch Innovation Monitoring for Robot Manipulation [4.53884951709371]
  We introduce, an action-chunk-conditioned latent patch innovation monitor for deployment-time intervention.<n>Experiments on real robot rollout data show that produces more stable and context-relevant triggers than competing runtime monitors.
  arXiv  Detail & Related papers  (2026-06-15T13:24:41Z)
• Phase-Conditioned Imitation Learning with Autonomous Failure Recovery for Robust Deformable Object Manipulation [5.524438437366922]
  We present a phase-conditioned, force-aware framework for deformable object manipulation.<n>A FiLM-conditioned ACT encoder modulates feature extraction based on the current task phase.<n>A multi-modal phase predictor fusing visual, force, and pose feedback estimates the phase in real time, detecting contact failures that are invisible to vision alone.
  arXiv  Detail & Related papers  (2026-05-28T05:59:53Z)
• Compiling Activation Steering into Weights via Null-Space Constraints for Stealthy Backdoors [48.881343993730844]
  Safety-aligned large language models (LLMs) are increasingly deployed in real-world pipelines.<n> adversaries can distribute backdoored checkpoints that behave normally under standard evaluation but jailbreak when a hidden trigger is present.<n>Recent post-hoc weight-editing methods offer an efficient approach to injecting such backdoors.
  arXiv  Detail & Related papers  (2026-04-14T06:48:33Z)
• A Physical Agentic Loop for Language-Guided Grasping with Execution-State Monitoring [5.437966695589128]
  We reformulate language-guided grasping as a bounded embodied agent operating over grounded execution states.<n>We introduce a physical agentic loop that wraps an unmodified learned manipulation primitive.<n>We validate the resulting loop on a mobile manipulator with an eye-in-hand D405 camera.
  arXiv  Detail & Related papers  (2026-04-08T08:01:35Z)
• Referring-Aware Visuomotor Policy Learning for Closed-Loop Manipulation [91.20850436220267]
  We introduce the Referring-Aware Visuomotor Policy (ReV)<n>ReV incorporates sparse referring points provided by a human or a high-level reasoning planner.<n>It is trained only by applying targeted perturbations to expert demonstrations.
  arXiv  Detail & Related papers  (2026-04-07T07:41:11Z)
• Detecting Object Tracking Failure via Sequential Hypothesis Testing [80.7891291021747]
  Real-time online object tracking in videos constitutes a core task in computer vision.<n>We propose interpreting object tracking as a sequential hypothesis test, wherein evidence for or against tracking failures is gradually accumulated over time.<n>We propose both supervised and unsupervised variants by leveraging either ground-truth or solely internal tracking information.
  arXiv  Detail & Related papers  (2026-02-13T14:57:15Z)
• Toward Operationalizing Rasmussen: Drift Observability on the Simplex for Evolving Systems [0.0]
  Monitoring drift into failure is hindered by Euclidean anomaly detection.<n>Rasmussen's dynamic safety model motivates drift under competing pressures.<n>We propose a vision for drift observability on the simplex.
  arXiv  Detail & Related papers  (2026-02-05T09:41:49Z)
• Detection and Recovery of Adversarial Slow-Pose Drift in Offloaded Visual-Inertial Odometry [0.0]
  Current trend of offloading VIO to edge servers can lead server-side threat surface.<n>We present an unsupervised, label-free detection and recovery mechanism.<n>We evaluate the approach in a realistic offloaded-VIO environment using ILLIXR testbed.
  arXiv  Detail & Related papers  (2025-09-08T18:31:40Z)
• Scene Graph-Guided Proactive Replanning for Failure-Resilient Embodied Agent [9.370683025542686]
  We present a proactive replanning framework that detects and corrects failures at subtask boundaries.<n>Experiments in the AI2-THOR simulator demonstrate that our approach detects semantic and spatial mismatches before execution failures occur.
  arXiv  Detail & Related papers  (2025-08-15T07:48:51Z)
• Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detection [56.66677293607114]
  We propose Code-as-Monitor (CaM) for both open-set reactive and proactive failure detection.<n>To enhance the accuracy and efficiency of monitoring, we introduce constraint elements that abstract constraint-related entities.<n>Experiments show that CaM achieves a 28.7% higher success rate and reduces execution time by 31.8% under severe disturbances.
  arXiv  Detail & Related papers  (2024-12-05T18:58:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.