Scalable Trajectory Generation for Whole-Body Mobile Manipulation
Robots deployed in unstructured environments must coordinate whole-body motion -- simultaneously moving a mobile base and arm -- to interact with the physical world. This coupled mobility and dexterity yields a state space that grows combinatorially with scene and object diversity, demanding datasets far larger than those sufficient for fixed-base manipulation. Yet existing acquisition methods, including teleoperation and planning, are either labor-intensive or computationally prohibitive at scale. The core bottleneck is the lack of a scalable pipeline for generating large-scale, physically valid, coordinated trajectory data across diverse embodiments and environments. Here we introduce A...
| Authors | Yida Niu, Xinhai Chang, Xin Liu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 14 April 2026 |
| Last updated at source | 14 April 2026 |
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