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.

本研究提出了M^3Bench，一个针对移动操控任务的全身运动生成基准测试，解决了现有模型在环境上下文及任务特定约束下协调运动的困难。研究中开发的M^3BenchMaker自动生成30,000个对象重排任务的运动轨迹，强调了发展新模型以应对这一挑战的必要性。通过这一基准，我们推动未来机器人研究 朝向更具适应性和能力的移动操控。

M${}^{3}$Bench：移动操控在三维场景下的全身运动生成基准测试