ACoT-VLA: Action Chain-of-Thought for Vision-Language-Action Models
Vision-Language-Action (VLA) models have emerged as essential generalist robot policies for diverse manipulation tasks, conventionally relying on directly translating multimodal inputs into actions via Vision-Language Model (VLM) embeddings. Recent advancements have introduced explicit intermediary reasoning, such as sub-task prediction (language) or goal image synthesis (vision), to guide action generation. However, these intermediate reasoning are often indirect and inherently limited in their capacity to convey the full, granular information required for precise action execution. Instead, we posit that the most effective form of reasoning is one that deliberates directly in the action space. We introduce Action Chain-of-Thought (ACoT), a paradigm where the reasoning process itself is formulated as a structured sequence of coarse action intents that guide the final policy. In this paper, we propose ACoT-VLA, a novel architecture that materializes the ACoT paradigm. Specifically, we introduce two complementary components: an Explicit Action Reasoner (EAR) and Implicit Action Reasoner (IAR). The former proposes coarse reference trajectories as explicit action-level reasoning steps, while the latter extracts latent action priors from internal representations of multimodal input, co-forming an ACoT that conditions the downstream action head to enable grounded policy learning. Extensive experiments in real-world and simulation environments demonstrate the superiority of our proposed method, which achieves 98.5%, 84.1%, and 47.4% on LIBERO, LIBERO-Plus and VLABench, respectively.
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Linqing Zhong
claimed_verified
2026-01-19T09:22:25.917Z
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Linqing Zhong
Linqing94482664
user
Linqing94482664
696d92733f1837bfb897095d
Yi Liu
696d92733f1837bfb897095e
Yifei Wei
696d92733f1837bfb897095f
Ziyu Xiong
admin_assigned
2026-01-19T14:40:12.039Z
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/avatars/10d4dff031f2d8dd7047d7ea5ec0d4c1.svg
Ziyu Xiong
JennyZiyu
user
JennyZiyu
696d92733f1837bfb8970960
Maoqing Yao
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Si Liu
696d92733f1837bfb8970962
Guanghui Ren
claimed_verified
2026-01-19T09:22:18.181Z
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/avatars/a17194be585d20e2a021e77a5a20e213.svg
Guanghui Ren
sundrops
user
sundrops
ACoT-VLA: Action Chain-of-Thought for Vision-Language-Action Models
Vision-Language-Action (VLA) models have emerged as essential generalist robot policies for diverse manipulation tasks, conventionally relying on directly translating multimodal inputs into actions via Vision-Language Model (VLM) embeddings. Recent advancements have introduced explicit intermediary reasoning, such as sub-task prediction (language) or goal image synthesis (vision), to guide action generation. However, these intermediate reasoning are often indirect and inherently limited in their capacity to convey the full, granular information required for precise action execution. Instead, we posit that the most effective form of reasoning is one that deliberates directly in the action space. We introduce Action Chain-of-Thought (ACoT), a paradigm where the reasoning process itself is formulated as a structured sequence of coarse action intents that guide the final policy. In this paper, we propose ACoT-VLA, a novel architecture that materializes the ACoT paradigm. Specifically, we introduce two complementary components: an Explicit Action Reasoner (EAR) and Implicit Action Reasoner (IAR). The former proposes coarse reference trajectories as explicit action-level reasoning steps, while the latter extracts latent action priors from internal representations of multimodal input, co-forming an ACoT that conditions the downstream action head to enable grounded policy learning. Extensive experiments in real-world and simulation environments demonstrate the superiority of our proposed method, which achieves 98.5%, 84.1%, and 47.4% on LIBERO, LIBERO-Plus and VLABench, respectively.
Vision-Language-Action models are enhanced by incorporating action-space reasoning through a structured sequence of coarse action intents, improving manipulation task performance in both simulation and real-world environments.
Vision-Language-Model action space action chain-of-thought ACoT Explicit Action Reasoner Implicit Action Reasoner coarse action intents multimodal input policy learning