title: Emma-X: An Embodied Multimodal Action Model with Grounded Chain of
    Thought and Look-ahead Spatial Reasoning
summary: 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.
paper_authors._id: 676196e778b836c285b3bc20
paper_authors.name: Qi Sun
paper_authors._id: 676196e778b836c285b3bc21
paper_authors.name: Pengfei Hong
paper_authors.status: admin_assigned
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paper_authors.fullname: Hong Pengfei
paper_authors.type: user
paper_authors.user: emrys-hong
paper_authors._id: 676196e778b836c285b3bc22
paper_authors.name: Tej Deep Pala
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paper_authors.fullname: Pala Tej Deep
paper_authors.type: user
paper_authors.user: Tej3
paper_authors._id: 676196e778b836c285b3bc23
paper_authors.name: Vernon Toh
paper_authors._id: 676196e778b836c285b3bc24
paper_authors.name: U-Xuan Tan
paper_authors._id: 676196e778b836c285b3bc25
paper_authors.name: Deepanway Ghosal
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-18T13:34:34.419Z
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paper_authors.fullname: Deepanway Ghosal
paper_authors.type: user
paper_authors.user: dghosal
paper_authors._id: 676196e778b836c285b3bc26
paper_authors.name: Soujanya Poria
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-18T13:34:27.305Z
paper_authors._id: 626b626405fe1cb65725aca1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/626b626405fe1cb65725aca1/aa-Lata46I3fXOmMetvXH.jpeg
paper_authors.fullname: Soujanya Poria
paper_authors.type: user
paper_authors.user: soujanyaporia
paper_title: Emma-X: An Embodied Multimodal Action Model with Grounded Chain of
    Thought and Look-ahead Spatial Reasoning
paper_summary: 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.
paper_ai_summary: Emma-X, a multimodal action model with grounded chain of thought and look-ahead spatial reasoning, improves long-horizon spatial reasoning and grounded task planning in robot manipulation using a hierarchical embodiment dataset and trajectory segmentation strategy.
paper_ai_keywords: Visual-Language-Action models hierarchical embodiment dataset BridgeV2 trajectory segmentation grippler states motion trajectories grounded task reasoning spatial guidance long-horizon spatial reasoning grounded task planning