SP-HLM: Sparse-Pose-Driven Motion Generation and Whole-Body Control for Humanoid Loco-Manipulation
Humanoid loco-manipulation requires an effective interface between high-level task policies and whole-body control.
ProofPaper ↗
Key points
- We present SP-HLM, a framework built on a sparse-pose-driven interface.
- SP-VLA, a vision-language-action model, first predicts task-relevant sparse poses and finger joint positions from multimodal observations and language instructions using asynchronous action-chunk inference.
- Given these sparse targets, SP-MGM, a motion generation model trained through two-stage distillation, reconstructs coordinated full-body motion in real time.
- Experiments in simulation and on a real humanoid robot demonstrate improved motion tracking, whole-body coordination, and loco-manipulation success over existing approaches.
Sources (1)
- [1]SP-HLM: Sparse-Pose-Driven Motion Generation and Whole-Body Control for Humanoid Loco-ManipulationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 05:03 PM
Humanoid loco-manipulation requires an effective interface between high-level task policies and whole-body control.
We present SP-HLM, a framework built on a sparse-pose-driven interface.
Extractive summary: sentences quoted from the sources.
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