让机器人在复杂环境中自主导航并灵巧地完成人机交互任务
HANDO: Hierarchical Autonomous Navigation and Dexterous Omni-loco-manipulation
- 分层设计：先自主探索定位目标，再协调四肢完成操作
- 可在动态环境里精准找到黑办公椅并递水给坐着的人
- 适合需要人机协作的移动机器人研发人员参考
在非结构化环境中实现无缝的移动操作，要求机器人结合自主探索与全身控制以进行物理交互。本文提出HANDO（分层自主导航与灵巧全向移动操作），一种专为带机械臂的腿式机器人设计的双层框架，用于执行以人为中心的移动操作任务。第一层采用目标条件化的自主探索策略，引导机器人在动态环境中定位语义指定的目标，如一张黑色办公椅。第二层则使用统一的全身移动操作策略，协调机械臂与腿部动作，完成精确交互任务，例如将饮料递给坐在椅子上的人。目前已初步部署导航模块，后续将推进更精细的全身协同操作部署。
原文摘要 · Abstract (English)
Seamless loco-manipulation in unstructured environments requires robots to leverage autonomous exploration alongside whole-body control for physical interaction. In this work, we introduce HANDO (Hierarchical Autonomous Navigation and Dexterous Omni-loco-manipulation), a two-layer framework designed for legged robots equipped with manipulators to perform human-centered mobile manipulation tasks. The first layer utilizes a goal-conditioned autonomous exploration policy to guide the robot to semantically specified targets, such as a black office chair in a dynamic environment. The second layer employs a unified whole-body loco-manipulation policy to coordinate the arm and legs for precise interaction tasks-for example, handing a drink to a person seated on the chair. We have conducted an initial deployment of the navigation module, and will continue to pursue finer-grained deployment of whole-body loco-manipulation.
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