PATCH: Action-Chunk-Conditioned Latent Patch Innovation Monitoring for Robot Manipulation

• URL: http://arxiv.org/abs/2606.16690v1
• Date: Mon, 15 Jun 2026 13:24:41 GMT
• Title: PATCH: Action-Chunk-Conditioned Latent Patch Innovation Monitoring for Robot Manipulation
• Abstract summary: 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.
• Score: 4.53884951709371
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning-based manipulation policies have made substantial progress in real-world robot manipulation, particularly for short-horizon action generation. However, deployment in open workspaces remains fragile under unexpected local scene dynamics, such as moving objects, transient occlusions, or disturbances near the intended motion. Existing runtime monitors often rely on global observation anomalies, policy uncertainty, or frame-level visual changes, and struggle to distinguish task-relevant execution risk from benign visual variation. We introduce PATCH, an action-chunk-conditioned latent patch innovation monitor for deployment-time intervention. Given the active action chunk, PATCH defines a projected execution corridor, predicts latent patch evolution inside it, and accumulates persistent residuals unexplained by the robot's own motion. These residuals form a localized intervention signal that allows PATCH-Router to pause execution, select an available recovery source, and resume the original policy once localized innovation subsides. Experiments on real robot rollout data show that PATCH produces more stable and context-relevant triggers than competing runtime monitors. Real-robot deployment further demonstrates monitor-driven intervention and policy resumption for disturbance-aware manipulation. Project Page: https://yananzhou5555.github.io/PATCH/.

Related papers

• AutoIntervene: Calibrated Intervention for Action-Chunking Imitation Learning Policies [4.786936073180585]
  Action-chunking visuomotor policies learn from demonstrations and improve temporal consistency.<n>We present AutoIntervene, an online framework that selectively transfers control between an action-chunking policy and an operator during deployment.
  arXiv  Detail & Related papers  (2026-08-07T10:14:29Z)
• SelfWAM: A Self-Grounded Unified World Action Model for Fast Robot Control [38.11007351456953]
  World Action Models (WAMs) improve robot policy learning by jointly modeling actions and future observations.<n>We introduce SelfWAM, a unified self-grounded WAM built on a modality-specialized Mixture-of-Transformers (MoT) architecture.<n>We show that SelfWAM produces more action-sensitive futures and preserves fast policy inference, while improving policy performance.
  arXiv  Detail & Related papers  (2026-08-01T15:47:29Z)
• 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)
• SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation [65.81476183180527]
  We introduce SegDiff, a closed-loop visuomotor policy that integrates the strengths of both paradigms.<n>SegDiff decomposes demonstrations into motion segments between keyposes and learns to predict the continuous trajectory from the current state to the next keypose.<n>SegDiff demonstrates significant performance gains over existing approaches across various simulated and real-world scenarios.
  arXiv  Detail & Related papers  (2026-07-13T02:48:03Z)
• VLA-Corrector: Lightweight Detect-and-Correct Inference for Adaptive Action Horizon [40.99993238069073]
  VLA-Corrector is a lightweight corrective inference framework for action-chunked Vision-Language-Action policies.<n>It can be integrated into different VLA models without further retraining the VLA backbone.<n>It mitigates the trade-off imposed by static horizons between execution and policy-call frequency.
  arXiv  Detail & Related papers  (2026-07-02T07:18:53Z)
• ActProbe: Action-Space Probe for Early Failure Detection of Generative Robot Policies [21.31011797815255]
  We introduce ActProbe, a pure action-space detector for generative robot policies.<n>It uses two compact signals available from a single forward pass: Temporal Consistency Error (TCE) between consecutive action chunks and Action Chunk Magnitude (ACM) of the current chunk.<n>It maps these signals to per-step failure probabilities with a task-conditioned LSTM-MLP architecture.
  arXiv  Detail & Related papers  (2026-06-07T08:18:11Z)
• ELAN4D: Embodiment-Centric 4D Supervision for Vision-Language-Action Models via Plug-and-Play Adaptation [63.617951135459016]
  Vision-Language-Action (VLA) models have shown promise for robotic manipulation, yet most existing policies reactively by directly regressing actions from current observations.<n>We propose ELAN4D, an embodiment-centric, 4D-aware training framework that enhances policies with future robot key tracks as predictive-temporal supervision.
  arXiv  Detail & Related papers  (2026-05-28T19:03:30Z)
• From Imagined Futures to Executable Actions: Mixture of Latent Actions for Robot Manipulation [88.39072412680633]
  We propose MoLA, a control-oriented interface that transforms imagined future videos into executable representations.<n>We evaluate our approach on simulated benchmarks and real-world robot manipulation tasks.
  arXiv  Detail & Related papers  (2026-05-12T14:15:16Z)
• Rewind-IL: Online Failure Detection and State Respawning for Imitation Learning [7.445072780282545]
  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.
  arXiv  Detail & Related papers  (2026-04-17T20:41:14Z)
• See, Plan, Rewind: Progress-Aware Vision-Language-Action Models for Robust Robotic Manipulation [59.07792608884117]
  We introduce See, Plan, Rewind (SPR), a progress-aware vision-language-action framework.<n>SPR operates through a continuous core cycle, Seeing the current state and upcoming milestone, Planning a trajectory towards the next 2D waypoint, and Rewinding to a recoverable state upon failure.<n>SPR achieves state-of-the-art robustness with the smallest performance drop, surpassing OpenVLA-OFT and UniVLA.
  arXiv  Detail & Related papers  (2026-03-10T07:22:51Z)
• Trace-Focused Diffusion Policy for Multi-Modal Action Disambiguation in Long-Horizon Robotic Manipulation [27.077503086179863]
  Trace-Focused Diffusion Policy (TF-DP) is a diffusion-based framework that explicitly conditions action generation on the robot's execution history.<n>We evaluate TF-DP on real-world robotic manipulation tasks exhibiting pronounced multi-modal action ambiguity and visually cluttered conditions.
  arXiv  Detail & Related papers  (2026-02-07T06:06:43Z)
• When Attention Betrays: Erasing Backdoor Attacks in Robotic Policies by Reconstructing Visual Tokens [32.50082492538355]
  Downstream fine-tuning of vision-language-action (VLA) models enhances robotics, yet exposes the pipeline to backdoor risks.<n>We introduce Bera, a test-time backdoor framework that detects tokens with anomalous attention via latent-space localization.<n>Bera effectively maintains nominal performance, significantly reduces attack success rates, and consistently restores benign behavior from backdoored outputs.
  arXiv  Detail & Related papers  (2026-02-03T06:09:43Z)
• 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.