M$^3$Bench: Benchmarking Whole-body Motion Generation for Mobile Manipulation in 3D Scenes
<|reference_start|>M$^3$Bench: Benchmarking Whole-body Motion Generation for Mobile Manipulation in 3D Scenes: We propose M^3Bench, a new benchmark for whole-body motion generation for mobile manipulation tasks. Given a 3D scene context, M^3Bench requires an embodied agent to understand its configuration, environmental constraints and task objectives, then generate coordinated whole-body motion trajectories for object rearrangement tasks. M^3Bench features 30k object rearrangement tasks across 119 diverse scenes, providing expert demonstrations generated by our newly developed M^3BenchMaker. This automatic data generation tool produces coordinated whole-body motion trajectories from high-level task instructions, requiring only basic scene and robot information. Our benchmark incorporates various task splits to assess generalization across different dimensions and leverages realistic physics simulation for trajectory evaluation. Through extensive experimental analyses, we reveal that state-of-the-art models still struggle with coordinated base-arm motion while adhering to environment-context and task-specific constraints, highlighting the need to develop new models that address this gap. Through M^3Bench, we aim to facilitate future robotics research towards more adaptive and capable mobile manipulation in diverse, real-world environments.<|reference_end|>
@article{zhang2024m3bench:,
    title={M3Bench: Benchmarking Whole-body Motion Generation for Mobile
  Manipulation in 3D Scenes},
    author={Zeyu Zhang, Sixu Yan, Muzhi Han, Zaijin Wang, Xinggang Wang, Song-Chun
  Zhu, Hangxin Liu},
    journal={arXiv preprint arXiv:2410.06678},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06678},
    primaryClass={cs.RO cs.AI cs.CV cs.LG}
}