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RACER: Rich Language-Guided Failure Recovery Policies for Imitation Learning
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Developing robust and correctable visuomotor policies for robotic manipulation is challenging due to the lack of self-recovery mechanisms from failures and the limitations of simple language instructions in guiding robot actions. To address these issues, we propose a scalable data generation pipeline that automatically augments expert demonstrations with failure recovery trajectories and fine-grained language annotations for training. We then introduce Rich languAge-guided failure reCovERy (RACER), a supervisor-actor framework, which combines failure recovery data with rich language descriptions to enhance robot control. RACER features a vision-language model (VLM) that acts as an online supervisor, providing detailed language guidance for error correction and task execution, and a language-conditioned visuomotor policy as an actor to predict the next actions. Our experimental results show that RACER outperforms the state-of-the-art Robotic View Transformer (RVT) on RLbench across various evaluation settings, including standard long-horizon tasks, dynamic goal-change tasks and zero-shot unseen tasks, achieving superior performance in both simulated and real world environments. Videos and code are available at: https://rich-language-failure-recovery.github.io.
Forward citations
Cited by 5 Pith papers
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Robix: A Unified Model for Robot Interaction, Reasoning and Planning
A three-stage-trained VLM unifies robot planning and dialogue, and beats commercial VLMs on the authors' interactive-task benchmarks.
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AimBot: A Simple Auxiliary Visual Cue to Enhance Spatial Awareness of Visuomotor Policies
Overlaying end-effector-derived shooting lines and reticles on RGB images consistently raises success rates of visuomotor policies, especially on long-horizon manipulation tasks.
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SwitchVLA: Execution-Aware Task Switching for Vision-Language-Action Models
SwitchVLA trains a vision-language-action policy to handle mid-execution instruction changes by conditioning on contact state and a three-way behavior mode, using only existing single-task demonstrations.
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LoHoVLA: A Unified Vision-Language-Action Model for Long-Horizon Embodied Tasks
A unified vision-language-action model that emits a sub-task description followed by a discrete action token outperforms modular and action-only baselines on simulated long-horizon tabletop tasks.
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Data Pyramid for Embodied Manipulation
Embodied training data form a five-layer pyramid—real-robot, UMI, ego/exo, simulation, general V–L—ordered by the trade-off between scale and robot alignment, and model capabilities track how those layers are mixed.
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