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About this paper
Zeyu Zhang, Sixu Yan, Muzhi Han, Zaijin Wang, Xinggang Wang, Song-Chun Zhu, Hangxin Liu
M³Bench provides a comprehensive benchmark and data generation tool for evaluating whole-body motion generation in mobile manipulation tasks within complex 3D scenes. Experiments using this platform demonstrate that state-of-the-art planning and learning methods achieve low success rates, particularly for placing objects and in novel environments, highlighting current limitations in coordinated robot behavior.
Key insights
- Current state-of-the-art hybrid methods, combining motion planning with affordance prediction, still yield low success rates, emphasizing the need for more holistic perception-to-action solutions.
- Generating feasible whole-body motion for mobile manipulation in diverse 3D scenes demands models with greater expressiveness and generalization capabilities than typically used for stationary manipulation.
- Placing objects is consistently more challenging than picking for all evaluated models, requiring more complex reasoning about stable placement and collision avoidance.
Summary by alphaXiv.
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