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Arxiv
3d
370
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Chain-of-Action: Trajectory Autoregressive Modeling for Robotic Manipulation
- Chain-of-Action (CoA) is a new visuo-motor policy paradigm based on Trajectory Autoregressive Modeling.
- CoA differs from traditional methods by generating an entire trajectory through backward reasoning with task-specific goals.
- The process involves an action-level Chain-of-Thought (CoT) within a single autoregressive structure.
- The first token in CoA represents a stable keyframe action encoding the task goals, with subsequent actions generated based on the initial keyframe and previously predicted actions.
- This backward action reasoning enforces a global-to-local structure, where each local action is tightly constrained by the final goal.
- To enhance action reasoning, CoA includes continuous action token representation, dynamic stopping for variable-length trajectory generation, reverse temporal ensemble, and multi-token prediction.
- CoA demonstrates strong spatial generalization capabilities while maintaining a flexible and simple visuo-motor policy.
- Empirical results show that CoA achieves state-of-the-art performance on 60 RLBench tasks and 8 real-world manipulation tasks.
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