发表机构
University College London（伦敦大学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究双足四足运动操纵设计权衡问题，核心方法是将特定操纵器集成到前小腿，实现双手地面操纵且四足着地、基座可行走，借助视觉语言模型排序技能，模拟中完成三项双手任务。
AI 中文摘要
大多数四足运动操纵设计在操纵能力和站立姿态之间进行权衡。安装在躯干上的手臂位置高且通常只有单臂；将腿部用作操纵器会使操纵腿离开地面；甚至安装在腿部的夹具也只能通过后腿站立来完成双手任务。本文将一个带有棱柱形滑块、两个旋转关节和一个夹具的操纵器集成到宇树Go2的每个前小腿中。两只手臂在地面抓取物体并双手操纵，同时四足都着地，无需站立。一只手臂搬运时，基座可自由行走。一个视觉语言模型根据头部相机图像和任务状态，在每个技能边界从预定义库中对技能进行排序，以实现长期自主。在模拟中，该设计执行了三项双手任务：自主技能选择下的长期橱柜任务、协作双手抬起和手臂间交接。
英文摘要
Most quadruped loco-manipulation designs trade manipulation capability against stance. A trunk-mounted arm sits high and usually carries a single arm; using the legs as manipulators lifts the manipulating leg off the ground; and even leg-mounted grippers reach two-handed tasks only by rearing onto the hind legs. This paper integrates a manipulator with a prismatic slider, two revolute joints, and a gripper into each front calf of a Unitree Go2. The two arms grasp objects at ground level and manipulate with both hands while all four feet stay planted, without rearing. With one arm carrying, the base stays free to walk. A vision-language model sequences skills from a predefined library at each skill boundary, conditioned on the head-camera image and task state, for long-horizon autonomy. In simulation, the design performs three bimanual tasks: a long-horizon cabinet task under autonomous skill selection, a cooperative two-handed lift, and an inter-arm handover.
Comments6 pages, 6 figures
