发表机构
Seoul National University（首尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出Co${}^{2}$Skill，通过统一策略整合全身场景交互与灵巧操作，利用任务相关观测掩码和跨任务课程，实现长时间顺序任务执行与场景交互，并在室内环境中验证技能组合。
AI 中文摘要
在复杂、非结构化环境中实现人类级别的灵巧性，需要无缝整合全身场景交互与灵巧物体操作技能。尽管现有的基于物理的控制器能在各自领域生成物理上合理的行为，但它们大多独立处理这两种能力。在本文中，我们提出Co${}^{2}$Skill，通过统一的策略公式整合场景交互与灵巧操作。基于预训练的运动先验，该策略使用任务和阶段相关的观测掩码来选择与当前交互目标相关的信息。我们引入了一个目标条件的移动操作课程，结合部分参考引导以实现精确性，以及从不同初始状态进行探索，同时允许超越演示轨迹的目标导向执行。我们进一步引入跨任务课程，联合训练单个技能和选定的任务序列，在任务边界间保持物理状态，并在后续场景交互中维持抓取。这些共同支持顺序任务执行以及场景交互与物体操作的同步进行。我们评估了坐、站、攀爬、楼梯穿越和目标导向操作，以及顺序执行和随机不同条件下的表现。此外，我们在室内环境中展示了技能组合，说明它们在相同控制公式内的整合。
英文摘要
Achieving human-level dexterity in complex, unstructured environments requires the seamless integration of whole-body scene interaction and dexterous object manipulation skills. While existing physics-based controllers generate physically plausible behaviors in each domain, they largely address these two capabilities independently. In this paper, we present Co${}^{2}$Skill that integrates scene interaction and dexterous manipulation through a unified policy formulation. Built on a pretrained motion prior, the policy uses task and phase dependent observation masks to select information relevant to the current interaction goals. We introduce a goal-conditioned loco-manipulation curriculum that combines partial reference guidance for precision with exploration from varied initial states while allowing goal-directed execution beyond the demonstrated trajectories. We further introduce a cross-task curriculum that jointly trains individual skills and selected task sequences, preserving physical states across task boundaries and maintaining grasps during subsequent scene interactions. Together, these support sequential task execution and simultaneous scene interaction with object manipulation. We evaluate sitting, standing, climbing, stair traversal, and goal-directed manipulation, together with sequential execution and with random different conditions. Additionally, we demonstrate skill compositions in indoor environments, illustrating their integration within the same control formulation.
Comments12 pages, 3 figures, 2 tables
