构建3万任务的机器人全身运动生成基准，推动真实场景下移动操作能力研究
M3Bench: Benchmarking Whole-body Motion Generation for Mobile Manipulation in 3D Scenes
- 基于30,000个任务和119个真实场景，自动合成全身运动轨迹数据
- 现有模型在协调机械臂与底盘运动时仍严重受限于环境约束
- 适合研究机器人自主操作、具身智能与物理仿真方向的学者使用
我们提出M3Bench，一个面向移动操作中全身运动生成的新基准。给定3D场景上下文，该基准要求具身智能体推理自身构型、环境约束与任务目标，生成物体重排所需的协调全身运动轨迹。M3Bench包含跨119个多样化场景的30,000个物体重排任务，其专家示范由新开发的M3BenchMaker自动生成——该工具仅需基础场景与机器人信息，即可从高层任务指令生成全身运动轨迹。基准涵盖多种任务划分以评估跨维度泛化能力，并采用真实物理仿真进行轨迹评估。大量实验分析表明，当前先进模型在协调基座-机械臂运动并遵守环境及任务特定约束方面仍表现不佳，凸显了新型模型的迫切需求。通过发布M3Bench与M3BenchMaker，我们旨在推动机器人研究向更适应、更强大的真实世界移动操作迈进。
原文摘要 · Abstract (English)
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.
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