Work

M${}^{3}$Bench: Benchmarking Whole-Body M otion Generation for M obile M anipulation in 3D Scenes

Year: 2025

Type: article

Abstract: We propose M${}^{3}$Bench, a new benchmark for whole-body motion generation in mobile manipulation tasks. Given a 3D scene context, M${}^{3}$Bench requires an embodied agent to reason about its configuration, environmental constraints, and task objectives to generate coordinated whole-body motion trajectories for object rearrangement. M${}^{3}$Bench features 30,000 object rearrangement tasks across 119 diverse scenes, providing expert demonstrations generated by our newly developed M${}^{3}$BenchMaker, 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 M${}^{3}$Bench and M${}^{3}$BenchMaker athttps://zeyuzhang.com/papers/m3bench, we aim to advance robotics research toward more adaptive and capable mobile manipulation in diverse, real-world environments.

Source: IEEE Robotics and Automation Letters

DOI: 10.1109/lra.2025.3575013