用统一模型实现人形机器人多任务控制，无需重新训练。
HOVER: Versatile Neural Whole-Body Controller for Humanoid Robots
- 以全身运动模仿为统一抽象，整合导航与操作等不同控制模式。
- 支持多种控制模式间无缝切换，保持各模式优势。
- 避免重复训练，提升人形机器人应用的效率与灵活性。
人形机器人全身体控需适应导航、行走操控和桌面操作等多种任务，每种任务对控制方式要求不同：导航依赖根部速度跟踪，而桌面操作则更关注上肢关节角度跟踪。现有方法通常针对特定指令空间训练独立策略，限制了跨模式迁移能力。本文提出关键洞察：全身体态运动模仿可作为所有任务的通用抽象，为学习多模式全身体控提供通用运动技能。基于此，我们构建了HOVER（人形通用控制器），一种多模式策略蒸馏框架，将多种控制模式整合为统一策略。该框架支持控制模式间的无缝切换，同时保留各模式的优势，为广泛控制模式下的人形机器人提供鲁棒且可扩展的解决方案。通过消除每种控制模式所需的策略重训练，本方法显著提升了未来人形机器人应用的效率与灵活性。
原文摘要 · Abstract (English)
Humanoid whole-body control requires adapting to diverse tasks such as navigation, loco-manipulation, and tabletop manipulation, each demanding a different mode of control. For example, navigation relies on root velocity tracking, while tabletop manipulation prioritizes upper-body joint angle tracking. Existing approaches typically train individual policies tailored to a specific command space, limiting their transferability across modes. We present the key insight that full-body kinematic motion imitation can serve as a common abstraction for all these tasks and provide general-purpose motor skills for learning multiple modes of whole-body control. Building on this, we propose HOVER (Humanoid Versatile Controller), a multi-mode policy distillation framework that consolidates diverse control modes into a unified policy. HOVER enables seamless transitions between control modes while preserving the distinct advantages of each, offering a robust and scalable solution for humanoid control across a wide range of modes. By eliminating the need for policy retraining for each control mode, our approach improves efficiency and flexibility for future humanoid applications.
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。