Emma-X：一种具有基于链式思维和前瞻空间推理的具身多模态行为模型

Emma-X: An Embodied Multimodal Action Model with Grounded Chain of Thought and Look-ahead Spatial Reasoning

December 16, 2024

作者: Qi Sun, Pengfei Hong, Tej Deep Pala, Vernon Toh, U-Xuan Tan, Deepanway Ghosal, Soujanya Poria

cs.AI

摘要

传统的基于强化学习的机器人控制方法通常是特定于任务的，无法在不同环境或未见过的物体和指令中实现泛化。视觉语言模型（VLMs）展现了强大的场景理解和规划能力，但缺乏生成针对特定机器人实体的可操作策略的能力。为解决这一问题，出现了视觉-语言-动作（VLA）模型，然而它们在长期空间推理和基于任务的规划方面面临挑战。在这项工作中，我们提出了具有基于链式思维和前瞻空间推理的具身多模态动作模型，Emma-X。Emma-X利用我们基于BridgeV2构建的分层实体数据集，其中包含60,000个机器人操作轨迹，自动注释了基于任务的推理和空间引导。此外，我们引入了基于夹爪状态和运动轨迹的轨迹分割策略，可以帮助减轻在生成基于地面任务推理的子任务推理时的幻觉。实验结果表明，Emma-X在需要空间推理的真实世界机器人任务中比竞争基准表现出更优异的性能。

English

Traditional reinforcement learning-based robotic control methods are often task-specific and fail to generalize across diverse environments or unseen objects and instructions. Visual Language Models (VLMs) demonstrate strong scene understanding and planning capabilities but lack the ability to generate actionable policies tailored to specific robotic embodiments. To address this, Visual-Language-Action (VLA) models have emerged, yet they face challenges in long-horizon spatial reasoning and grounded task planning. In this work, we propose the Embodied Multimodal Action Model with Grounded Chain of Thought and Look-ahead Spatial Reasoning, Emma-X. Emma-X leverages our constructed hierarchical embodiment dataset based on BridgeV2, containing 60,000 robot manipulation trajectories auto-annotated with grounded task reasoning and spatial guidance. Additionally, we introduce a trajectory segmentation strategy based on gripper states and motion trajectories, which can help mitigate hallucination in grounding subtask reasoning generation. Experimental results demonstrate that Emma-X achieves superior performance over competitive baselines, particularly in real-world robotic tasks requiring spatial reasoning.