arXiv cs.AI· Zhaohui Yang, Quanbao Lin, Chengqi Shi, Zhenglang Weng, Jicheng Lin, Boyu Chen, Tengjie Zhu, Guanzhu Ren, Jiahao Xu, Yang Feng, Xiaokang Yang, Yao Mu·· 13 小时前AI 评分31
SP-HLM：稀疏姿态驱动的人形机器人移动操作运动生成与全身控制框架
SP-HLM: Sparse-Pose-Driven Motion Generation and Whole-Body Control for Humanoid Loco-Manipulation
AI 导读
SP-HLM 提出以稀疏姿态驱动的接口连接高层任务策略与全身控制，用五个任务关键身体姿态结合学习到的隐式运动先验来表达空间意图与全身协调。其中 SP-VLA 从多模态观测和语言指令预测稀疏姿态与手指关节位置，SP-MGM 经两阶段蒸馏实时重建全身运动，再由 SP-GMT 融合显式稀疏目标与隐式运动先验完成全身控制。仿真与真实人形机器人实验显示，其在运动跟踪、全身协调和移动操作成功率上优于现有方法。
正文 · 原文
Authors:Zhaohui Yang, Quanbao Lin, Chengqi Shi, Zhenglang Weng, Jicheng Lin, Boyu Chen, Tengjie Zhu, Guanzhu Ren, Jiahao Xu, Yang Feng, Xiaokang Yang, Yao Mu
Abstract:Humanoid loco-manipulation requires an effective interface between high-level task policies and whole-body control. Existing interfaces typically rely on a single action representation for both task intent and whole-body coordination. This may burden the high-level policy or leave one aspect underspecified. We present SP-HLM, a framework built on a sparse-pose-driven interface. The interface combines five task-critical body poses with a learned latent motion prior to represent spatial intent and whole-body coordination in complementary forms. 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. A pretrained motion encoder maps this motion to a compact latent representation. SP-GMT, a general motion tracker, then integrates the explicit sparse targets with this implicit motion prior for whole-body control. Experiments in simulation and on a real humanoid robot demonstrate improved motion tracking, whole-body coordination, and loco-manipulation success over existing approaches. Additional results and videos are available on our \href{this https URL}{\textcolor{blue}{project page}}.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.30044 [cs.RO] |
| (or arXiv:2610.30044v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.30044 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhaohui Yang [view email]
[v1]
Wed, 21 Oct 2026 17:03:34 UTC (12,747 KB)
来源：arXiv cs.AI · arxiv.org