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DualCoT-VLA: 通过并行推理实现视觉-语言链式思维的视觉-语言-动作模型

DualCoT-VLA: Visual-Linguistic Chain of Thought via Parallel Reasoning for Vision-Language-Action Models

The Hong Kong University of Science and Technology (Guangzhou)(香港科学与技术大学(广州));Huawei Foundation Model Department(华为基础模型部门)

Zhide Zhong, Junfeng Li, Junjie He, Haodong Yan, Xin Gong, Guanyi Zhao, Yingjie Cai, Jiantao Gao, Xu Yan, Bingbing Liu, Yingcong Chen, Liuqing Yang, Haoang Li

AI总结:

DualCoT-VLA通过并行推理机制,结合视觉和语言链式思维,解决传统VLA模型在复杂多步骤任务和精细空间感知中的不足,实现更高效的视觉-语言-动作处理。

摘要:

视觉-语言-动作(VLA)模型将视觉观测和语言指令直接映射到机器人动作。尽管在简单任务中有效,标准VLA模型在需要逻辑规划的复杂多步骤任务以及需要精细空间感知的精确操作中常常遇到困难。最近的努力将链式思维(CoT)推理引入VLA模型,以赋予其“在行动前思考”的能力。然而,当前基于CoT的VLA模型面临两个关键限制:1)由于依赖于孤立的单模态CoT,无法同时捕捉低级视觉细节和高级逻辑规划;2)由于逐步自回归解码,导致高推理延迟和累积误差。为了解决这些限制,我们提出了DualCoT-VLA,一种用于VLA模型的视觉-语言CoT方法,具有并行推理机制。为了实现全面的多模态推理,我们的方法整合了用于低级空间理解的视觉CoT和用于高级任务规划的语言CoT。此外,为了克服延迟瓶颈,我们引入了并行CoT机制,该机制结合了两组可学习的查询标记,将自回归推理转换为单步前向推理。广泛的实验表明,我们的DualCoT-VLA在LIBERO和RoboCasa GR1基准以及现实世界平台中均实现了最先进的性能。

英文摘要:

Vision-Language-Action (VLA) models map visual observations and language instructions directly to robotic actions. While effective for simple tasks, standard VLA models often struggle with complex, multi-step tasks requiring logical planning, as well as precise manipulations demanding fine-grained spatial perception. Recent efforts have incorporated Chain-of-Thought (CoT) reasoning to endow VLA models with a ``thinking before acting'' capability. However, current CoT-based VLA models face two critical limitations: 1) an inability to simultaneously capture low-level visual details and high-level logical planning due to their reliance on isolated, single-modal CoT; 2) high inference latency with compounding errors caused by step-by-step autoregressive decoding. To address these limitations, we propose DualCoT-VLA, a visual-linguistic CoT method for VLA models with a parallel reasoning mechanism. To achieve comprehensive multi-modal reasoning, our method integrates a visual CoT for low-level spatial understanding and a linguistic CoT for high-level task planning. Furthermore, to overcome the latency bottleneck, we introduce a parallel CoT mechanism that incorporates two sets of learnable query tokens, shifting autoregressive reasoning to single-step forward reasoning. Extensive experiments demonstrate that our DualCoT-VLA achieves state-of-the-art performance on the LIBERO and RoboCasa GR1 benchmarks, as well as in real-world platforms.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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