FLARE: A Failure-Aware Framework for Autonomous Correction and Recovery in Visual-Language Robotic Manipulation

• URL: http://arxiv.org/abs/2608.26645v1
• Date: Thu, 27 Aug 2026 05:58:06 GMT
• Title: FLARE: A Failure-Aware Framework for Autonomous Correction and Recovery in Visual-Language Robotic Manipulation
• Abstract summary: 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.
• Score: 43.89036551517797
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language-Action Models~(VLAs) have demonstrated significant promise in generalizing to complex, long-horizon robotic manipulation tasks. However, their performance remains brittle, as they are typically trained on trajectory-monotonic, failure-free demonstrations. This reliance on ``perfect" data leaves them unable to recover from common execution errors, such as a missed grasp, a dropped object, or an unexpected collision. In this paper, we propose FLARE, a novel framework that endows VLAs with robust error recovery capabilities through a ``Retry" and ``Reset" paradigm. First, we introduce a ``Retry" mechanism by injecting perturbation and bridging segments that decouple robot pose from environment state into demonstrations, enabling the policy to autonomously handle execution deviations. Second, to address critical, state-breaking (OOD) failures, we introduce a ``Reset" pipeline. We leverage an MLLM for offline failure analysis to automatically identify OOD states from execution videos. This analysis enables the efficient, targeted collection of a small library of object-centric ``Reset" skills, which are trained to restore the environment to a task-valid state. Our full framework integrates these learned policies. At inference, an online MLLM monitor arbitrates between task execution and ``Reset" skills. Experiments on challenging, contact-rich manipulation tasks show our approach significantly improves task success and robustness.

Related papers

• TrapVLA: Trapping Vision-Language-Action Models in Configured Failure Modes [65.10128145059127]
  This work introduces Configured Failure Trapping, a novel backdoor attack task against Vision-Language-Action (VLA) models.<n>We propose an effective data engine for synthesizing high-quality target trajectories and an automated suite for measuring configured-failure fidelity.<n>Experiments show that TrapVLA effectively injects configured failure modes into VLA models while largely preserving performance on clean data.
  arXiv  Detail & Related papers  (2026-08-27T03:44:49Z)
• A Few Words Go a Long Way: Language Guided Robot Policy Synthesis [9.088404024682706]
  ARCHITECT is a framework that treats robot policy acquisition as an interactive program synthesis task.<n>We introduce an iterative process where a human supervisor provides natural language corrections to steer the policy.<n>In a benchmark evaluation on a Franka Panda robot, ARCHITECT outperforms state-of-the-art VLA models.
  arXiv  Detail & Related papers  (2026-07-26T18:00:02Z)
• 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)
• RADAR: Closed-Loop Robotic Data Generation via Semantic Planning and Autonomous Causal Environment Reset [48.645870795753105]
  We introduce Robust Autonomous Data Acquisition for Robotics (RADAR)<n>RADAR is a fully autonomous, closed-loop data generation engine that completely removes human intervention from the collection cycle.<n>In simulation, our framework achieves up to 90% success rates on complex, long-horizon tasks.
  arXiv  Detail & Related papers  (2026-03-12T11:18:52Z)
• 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)
• Guardian: Detecting Robotic Planning and Execution Errors with Vision-Language Models [53.20969621498248]
  We propose an automatic robot failure synthesis approach that procedurally perturbs successful trajectories to generate diverse planning and execution failures.<n>We construct three new failure detection benchmarks: RLBench-Fail, BridgeDataV2-Fail, and UR5-Fail.<n>We then train Guardian, a VLM with multi-view images for detailed failure reasoning and detection.
  arXiv  Detail & Related papers  (2025-12-01T17:57:27Z)
• A Unified Framework for Real-Time Failure Handling in Robotics Using Vision-Language Models, Reactive Planner and Behavior Trees [1.3481665321936716]
  This paper presents a unified failure recovery framework that combines Vision-Language Models (VLMs), a reactive planner, and Behavior Trees (BTs) to enable real-time failure handling.<n>Our approach includes pre-execution verification, which checks for potential failures before execution, and reactive failure handling, which detects and corrects failures during execution.<n>We evaluate our framework through real-world experiments with an ABB YuMi robot on tasks like peg insertion, object sorting, and drawer placement.
  arXiv  Detail & Related papers  (2025-03-19T13:40:56Z)
• RACER: Rich Language-Guided Failure Recovery Policies for Imitation Learning [19.023560632891467]
  We propose a scalable data generation pipeline that augments expert demonstrations with failure recovery trajectories. We then introduce Rich languAge-guided failure reCovERy (RACER), a supervisor-actor framework. Our experimental results show that RACER outperforms the state-of-the-art Robotic View Transformer on RLbench.
  arXiv  Detail & Related papers  (2024-09-23T02:50:33Z)
• Learning to Recover from Plan Execution Errors during Robot Manipulation: A Neuro-symbolic Approach [7.768747914019512]
  We propose an approach (blending learning with symbolic search) for automated error discovery and recovery. We present an anytime version of our algorithm, where instead of recovering to the last correct state, we search for a sub-goal in the original plan.
  arXiv  Detail & Related papers  (2024-05-29T10:03:57Z)

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.