Chain-of-Action: Trajectory Autoregressive Modeling for Robotic Manipulation

• URL: http://arxiv.org/abs/2506.09990v1
• Date: Wed, 11 Jun 2025 17:59:13 GMT
• Title: Chain-of-Action: Trajectory Autoregressive Modeling for Robotic Manipulation
• Authors: Wenbo Zhang, Tianrun Hu, Yanyuan Qiao, Hanbo Zhang, Yuchu Qin, Yang Li, Jiajun Liu, Tao Kong, Lingqiao Liu, Xiao Ma,
• Abstract summary: Chain-of-Action (CoA) is a visuo-motor policy paradigm built upon Trajectory Autoregressive Modeling.<n>CoA generates an entire trajectory by explicit backward reasoning with task-specific goals.<n>We observe CoA the state-of-the-art performance across 60 RLBench tasks and 8 real-world manipulation tasks.
• Score: 37.748111048944274
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Chain-of-Action (CoA), a novel visuo-motor policy paradigm built upon Trajectory Autoregressive Modeling. Unlike conventional approaches that predict next step action(s) forward, CoA generates an entire trajectory by explicit backward reasoning with task-specific goals through an action-level Chain-of-Thought (CoT) process. This process is unified within a single autoregressive structure: (1) the first token corresponds to a stable keyframe action that encodes the task-specific goals; and (2) subsequent action tokens are generated autoregressively, conditioned on the initial keyframe and previously predicted actions. This backward action reasoning enforces a global-to-local structure, allowing each local action to be tightly constrained by the final goal. To further realize the action reasoning structure, CoA incorporates four complementary designs: continuous action token representation; dynamic stopping for variable-length trajectory generation; reverse temporal ensemble; and multi-token prediction to balance action chunk modeling with global structure. As a result, CoA gives strong spatial generalization capabilities while preserving the flexibility and simplicity of a visuo-motor policy. Empirically, we observe CoA achieves the state-of-the-art performance across 60 RLBench tasks and 8 real-world manipulation tasks.

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