外观
论文arXiv 2609.305941 个月前
HuGo：LLM 作为人形机器人移动操作全身策略代码设计器
原文链接：HuGo: LLMs as Whole-Body Policy Code Designers for Humanoid Loco-Manipulation（arXiv 2609.30594 · 发布日期：2026-09-24）
HuGo: LLMs as Whole-Body Policy Code Designers for Humanoid Loco-Manipulation
HuGo用LLM在冻结低层全身策略上生成并迭代高层代码，面向人形移动操作，在仿真任务与硬件迁移中验证。
核心要点
- 问题在于现有人形移动操作方法依赖奖励工程或示教及任务特定训练，扩展到新任务成本较高。
- HuGo根据任务描述、观测和命令规格，让LLM生成可执行闭环高层策略代码，并构建任务逻辑。
- 系统利用rollout中的数值轨迹和选定视频帧反馈，针对性更新代码以细化高层策略。
- 在五个仿真任务和两种低层策略上，HuGo优于高层强化学习基线并接近示教基线；项目网站已给出。
验证范围与局限：验证范围包括五个仿真任务、两种低层策略，以及从仿真到硬件的零样本迁移和真实rollout细化。
英文摘要 (Abstract)
For humanoids to be useful in everyday environments, they must perform a wide range of tasks that couple locomotion and manipulation. Existing approaches commonly acquire a loco-manipulation policy through reward engineering or demonstrations followed by task-specific training, making it costly to scale to new tasks. In this work, we propose a hierarchical approach to humanoid loco-manipulation that eliminates these per-task requirements. HuGo, Humanoid policy code Generation, uses a Large Language Model (LLM) to generate executable, closed-loop high-level policy code from a task description on top of a frozen low-level whole-body policy. Given the task, observation, and command specifications, the LLM constructs the task logic in code. HuGo then refines the policy from its rollouts using numerical trajectories and selected video frames to produce feedback and targeted code updates. Across five simulation tasks, using two different low-level policies, HuGo substantially outperforms a high-level reinforcement learning baseline and approaches the performance of a demonstration-based baseline. We achieve this level of performance without task-specific reward design or demonstration collection. We further demonstrate zero-shot transfer of simulation-generated policies to hardware and show that applying the same refinement loop to real-world rollouts can further improve transfer performance without expert demonstrations or policy retraining. Project website is https://iconlab.negarmehr.com/HuGo/
作者：Seoyeon Choi, Shizhao Ye, Nicholas Bui, Aayushi Shrivastava, Kanghyun Ryu, Dhruva Tirumala, Markus Wulfmeier, Negar Mehr
来源
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