ULTRA trains Unitree G1 humanoids for autonomous whole-body loco-manipulation with one controller
机器之心 · wechat · 2026-09-25
UIUC researchers present ULTRA (Unified Multimodal Control for Autonomous Humanoid Whole-Body Loco-Manipulation), validated on a Unitree G1 humanoid. Accepted at IROS 2026 and shortlisted for the Mobile Manipulation Paper Award.
Key points:
- One controller, multiple instruction granularities: a single set of policy parameters handles both frame-level motion tracking and goal-driven control where only a sparse target (e.g. box destination) is given.
- Physics-based neural motion retargeting: RL in simulation converts human mocap data into executable robot trajectories that respect dynamics and contact constraints; the same retargeting policy generalizes to new motions and object sizes without retraining.
- Two-stage learning: a teacher policy with full state/reference access is distilled into a student using input-availability masks, then RL fine-tuning adds robustness to perturbations and goal changes.
- Egocentric depth perception: object point clouds extracted from depth images replace external mocap; RL fine-tuning substantially improved success rates with point-cloud inputs.
Authors He Xialin and Xu Sirui are UIUC CS PhD students. Paper and project page are public.
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