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October 3, 2025Open Access

EmbRACE-3K: Embodied Reasoning and Action in Complex Environments

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Authors

MLMaohui LinNanfang HospitalWHWei HuangEdith Cowan UniversityYLYi LiChongqing Normal University

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Implication

Dataset EmRACE-3K bridges the gap in embodied reasoning, enhancing spatial and goal-directed tasks in VLMs.

Key Points

  • Success rates for VLMs in zero-shot settings are under 20%, indicating significant challenges in interactive environments.
  • Over 3,000 language-guided tasks in diverse environments were developed to test embodied reasoning capabilities.
  • Supervised and reinforcement learning on Qwen2.5-VL-7B showed considerable improvements in navigation and object manipulation tasks.
  • The study highlights limitations of current vision-language models in complex, dynamic settings and aims to address them.

Cite This Study

Lin et al. (2025) studied this question.

synapsesocial.com/papers/68e040eda99c246f578b3377https://doi.org/10.48550/arxiv.2507.10548
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Embodied Understanding of Driving Scenarios2024 · 2 citations
  2. 2LLM-Based Control for Simulated Physical Reasoning: Modular Evaluation in the NeurIPS Embodied Agent Interface Challenge2026
  3. 3EmboMatrix: A Scalable Training-Ground for Embodied Decision-Making2025
  4. 4PlanAgent: Embodied Visual-Language Model for Grounded Task planning with Environment Map2024
  5. 5Visual Embodied Brain: Let Multimodal Large Language Models See, Think, and Control in Spaces2025