用统一控制器实现人形机器人长时间积木重排，提升任务连续性。
Humanoid Hanoi: Investigating Shared Whole-Body Control for Skill-Based Box Rearrangement
- 所有技能共享一个通用全身控制器，统一执行接口
- 通过闭环技能训练数据增强，提升长期任务鲁棒性
- 在模拟和Digit V3机器人上实现全自动长程重排
我们提出一种基于技能的框架，用于人形机器人积木重排，通过在任务层面组合可复用技能实现长时序执行。所有技能均通过一个共享的、任务无关的全身控制器（WBC）执行，提供一致的闭环接口；相比非共享设计中各技能使用独立低层控制器的方式更具一致性。我们发现，直接复用预训练的共享WBC会导致长期任务中鲁棒性下降，因新技能及其组合引发状态与指令分布偏移。为此，我们引入一种简单的数据聚合方法，在领域随机化条件下，将闭环技能执行的回放轨迹用于增强共享WBC训练。为评估该方法，我们构建了名为Humanoid Hanoi的长时序汉诺塔式积木重排基准，实验在仿真环境和Digit V3人形机器人上进行，成功实现完全自主的长时序重排，并量化验证了共享WBC相比非共享基线的优势。
原文摘要 · Abstract (English)
We investigate a skill-based framework for humanoid box rearrangement that enables long-horizon execution by sequencing reusable skills at the task level. In our architecture, all skills execute through a shared, task-agnostic whole-body controller (WBC), providing a consistent closed-loop interface for skill composition, in contrast to non-shared designs that use separate low-level controllers per skill. We find that naively reusing the same pretrained WBC can reduce robustness over long horizons, as new skills and their compositions induce shifted state and command distributions. We address this with a simple data aggregation procedure that augments shared-WBC training with rollouts from closed-loop skill execution under domain randomization. To evaluate the approach, we introduce Humanoid Hanoi, a long-horizon Tower-of-Hanoi box rearrangement benchmark, and report results in simulation and on the Digit V3 humanoid robot, demonstrating fully autonomous rearrangement over extended horizons and quantifying the benefits of the shared-WBC approach over non-shared baselines. Project page: https://osudrl.github.io/Humanoid_Hanoi/
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。