M3Bench: Benchmarking Whole-body Motion Generation for Mobile Manipulation in 3D Scenes
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Abstract (arXiv)
We propose M3Bench, a new benchmark for whole-body motion generation in mobile manipulation tasks. Given a 3D scene context, M3Bench requires an embodied agent to reason about its configuration, environmental constraints, and task objectives to generate coordinated whole-body motion trajectories for object rearrangement. M3Bench features 30,000 object rearrangement tasks across 119 diverse scenes, providing expert demonstrations generated by our newly developed M3BenchMaker, an automatic data generation tool that produces whole-body motion trajectories from high-level task instructions using only basic scene and robot information. Our benchmark includes various task splits to evaluate generalization across different dimensions and leverages realistic physics simulation for trajectory assessment. Extensive evaluation analysis reveals that state-of-the-art models struggle with coordinating base-arm motion while adhering to environmental and task-specific constraints, underscoring the need for new models to bridge this gap. By releasing M3Bench and M3BenchMaker we aim to advance robotics research toward more adaptive and capable mobile manipulation in diverse, real-world environments.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 41 of its 54 references that are in the map. First posted on arXiv 9 Oct 2024.
Builds on
41 of 54 references.
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29 more
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Built on by
Citing papers per year: 2024: 2; 2025: 2; 2026: 2.
6 papers.
- 2024M2Diffuser: Diffusion-based Trajectory Optimization for Mobile Manipulation in 3D Scenescited by 27
- 2025ManiTaskGen: A Comprehensive Task Generator for Benchmarking and Improving Vision-Language Agents on Embodied Decision-Makingcited by 4
- 2024λ: A Benchmark for Data-Efficiency in Long-Horizon Indoor Mobile Manipulation Roboticscited by 3
- 2025Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AIcited by 3
- 2026Scalable Trajectory Generation for Whole-Body Mobile Manipulationcited by 0
- 2026RLMM-Flow: A Flow-based Mobile Manipulation Framework with Latent-Space Reinforcement Learningcited by 0
- 2026RLMM-Flow: A Flow-based Mobile Manipulation Framework with Latent-Space Reinforcement Learning29 Jul 2026
- 2026Scalable Trajectory Generation for Whole-Body Mobile Manipulation14 Apr 2026
- 2025Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI6 Oct 2025
- 2025ManiTaskGen: A Comprehensive Task Generator for Benchmarking and Improving Vision-Language Agents on Embodied Decision-Making27 May 2025
- 2024λ: A Benchmark for Data-Efficiency in Long-Horizon Indoor Mobile Manipulation Robotics28 Nov 2024
- 2024M2Diffuser: Diffusion-based Trajectory Optimization for Mobile Manipulation in 3D Scenes15 Oct 2024