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

arXiv: https://arxiv.org/abs/2412.11974 · HTML: https://mlsanity.com/paper/2412.11974/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 13 of its 14 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 13 of 13)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Octo: An Open-Source Generalist Robot Policy](https://mlsanity.com/paper/2405.12213/) (2024)
- [BridgeData V2: A Dataset for Robot Learning at Scale](https://mlsanity.com/paper/2308.12952/) (2023)
- [A Survey on Vision-Language-Action Models for Embodied AI](https://mlsanity.com/paper/2405.14093/) (2024)
- [RT-H: Action Hierarchies Using Language](https://mlsanity.com/paper/2403.01823/) (2024)
- [LLARVA: Vision-Action Instruction Tuning Enhances Robot Learning](https://mlsanity.com/paper/2406.11815/) (2024)
- [LLaRA: Supercharging Robot Learning Data for Vision-Language Policy](https://mlsanity.com/paper/2406.20095/) (2024)

## Built on by (most cited first, 30 of 47)

- [MolmoAct: Action Reasoning Models that can Reason in Space](https://mlsanity.com/paper/2508.07917/) (2025)
- [Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey](https://mlsanity.com/paper/2503.12605/) (2025)
- [Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models](https://mlsanity.com/paper/2505.04921/) (2025)
- [Vision-Language-Action Models for Robotics: A Review Towards Real-World Applications](https://mlsanity.com/paper/2510.07077/) (2025)
- [InstructVLA: Vision-Language-Action Instruction Tuning from Understanding to Manipulation](https://mlsanity.com/paper/2507.17520/) (2025)
- [A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agents](https://mlsanity.com/paper/2601.12538/) (2026)
- [MolmoAct2: Action Reasoning Models for Real-world Deployment](https://mlsanity.com/paper/2605.02881/) (2026)
- [SIMA 2: A Generalist Embodied Agent for Virtual Worlds](https://mlsanity.com/paper/2512.04797/) (2025)
- [NORA-1.5: A Vision-Language-Action Model Trained using World Model- and Action-based Preference Rewards](https://mlsanity.com/paper/2511.14659/) (2025)
- [Vlaser: Vision-Language-Action Model with Synergistic Embodied Reasoning](https://mlsanity.com/paper/2510.11027/) (2025)
- [Latent Reasoning VLA: Latent Thinking and Prediction for Vision-Language-Action Models](https://mlsanity.com/paper/2602.01166/) (2026)
- [RoVer: Robot Reward Model as Test-Time Verifier for Vision-Language-Action Model](https://mlsanity.com/paper/2510.10975/) (2025)
- [G0.5: One Autoregressive Stream for Robot Reasoning and Action](https://mlsanity.com/paper/2608.11739/) (2026)
- [Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends](https://mlsanity.com/paper/2506.20966/) (2025)
- [MolmoPoint: Better Pointing for VLMs with Grounding Tokens](https://mlsanity.com/paper/2603.28069/) (2026)
- [Unifying Perception and Action: A Hybrid-Modality Pipeline with Implicit Visual Chain-of-Thought for Robotic Action Generation](https://mlsanity.com/paper/2511.19859/) (2025)
- [Mantis: A Versatile Vision-Language-Action Model with Disentangled Visual Foresight](https://mlsanity.com/paper/2511.16175/) (2025)
- [DualVLA: Building a Generalizable Embodied Agent via Partial Decoupling of Reasoning and Action](https://mlsanity.com/paper/2511.22134/) (2025)
- [PhysBrain: Human Egocentric Data as a Bridge from Vision Language Models to Physical Intelligence](https://mlsanity.com/paper/2512.16793/) (2025)
- [ERA: Transforming VLMs into Embodied Agents via Embodied Prior Learning and Online Reinforcement Learning](https://mlsanity.com/paper/2510.12693/) (2025)
- [TTT-VLA: Test-Time Latent Prompt Optimization for Vision-Language-Action Models](https://mlsanity.com/paper/2606.03127/) (2026)
- [Mind to Hand: Purposeful Robotic Control via Embodied Reasoning](https://mlsanity.com/paper/2512.08580/) (2025)
- [Survey of Vision-Language-Action Models for Embodied Manipulation](https://mlsanity.com/paper/2508.15201/) (2025)
- [PoseVLA: Universal Pose Pretraining for Generalizable Vision-Language-Action Policies](https://mlsanity.com/paper/2602.19710/) (2026)
- [Revisiting Embodied Chain-of-Thought for Generalizable Robot Manipulation](https://mlsanity.com/paper/2606.03784/) (2026)
- [ABot-N1: Toward a General Visual Language Navigation Foundation Model](https://mlsanity.com/paper/2607.10383/) (2026)
- [TRAP: Hijacking VLA CoT-Reasoning via Adversarial Patches](https://mlsanity.com/paper/2603.23117/) (2026)
- [Transferring Vision-Language-Action Models to Industry Applications: Architectures, Performance, and Challenges](https://mlsanity.com/paper/2509.23121/) (2025)
- [DextER: Language-driven Dexterous Grasp Generation with Embodied Reasoning](https://mlsanity.com/paper/2601.16046/) (2026)
- [Self-Supervised Bootstrapping of Action-Predictive Embodied Reasoning](https://mlsanity.com/paper/2602.08167/) (2026)

## Abstract (arXiv)

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.