M3Bench: Benchmarking Whole-body Motion Generation for Mobile Manipulation in 3D Scenes

TL;DR

M3Bench is a comprehensive benchmark with a large dataset for evaluating whole-body motion generation in mobile manipulation tasks within 3D scenes, highlighting current model limitations.

Contribution

We introduce M3Bench, a novel benchmark with an automatic data generator for assessing whole-body motion in diverse 3D environments, promoting progress in mobile manipulation research.

Findings

State-of-the-art models struggle with coordination and environmental constraints.

M3Bench provides extensive data for evaluating generalization.

Benchmark facilitates development of more adaptive mobile manipulation models.

Abstract

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.