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

TL;DR

Emma-X is a novel embodied multimodal model that enhances robotic spatial reasoning and grounded task planning by leveraging hierarchical datasets and trajectory segmentation, outperforming existing methods in real-world robotic tasks.

Contribution

The paper introduces Emma-X, a new embodied multimodal action model with grounded chain of thought and look-ahead spatial reasoning, utilizing a large hierarchical dataset and a trajectory segmentation strategy.

Findings

Emma-X outperforms baseline models in real-world robotic spatial reasoning tasks.

The hierarchical dataset improves grounding accuracy and task understanding.

Trajectory segmentation reduces hallucinations in subtask reasoning.

Abstract

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.