We present Adaptive Skill Coordination (ASC) -- an approach for accomplishing long-horizon tasks like mobile pick-and-place (i.e., navigating to an object, picking it, navigating to another location, and placing it). ASC consists of three components -- (1) a library of basic visuomotor skills (navigation, pick, place), (2) a skill coordination policy that chooses which skill to use when, and (3) a corrective policy that adapts pre-trained skills in out-of-distribution states. All components of ASC rely only on onboard visual and proprioceptive sensing, without requiring information like detailed maps with obstacle layouts or precise object locations, easing real-world deployment. We train ASC in simulated indoor environments, and deploy it zero-shot (without any real-world experience or fine-tuning) on the Boston Dynamics Spot robot in 8 novel real-world environments (1 apartment, 1 lab, 2 microkitchens, 2 lounges, 1 office space, 1 outdoor courtyard). In rigorous quantitative comparisons in 2 environments, ASC achieves near-perfect performance (59/60 episodes, or 98%), while sequentially executing skills succeeds in only 44/60 (73%) episodes. Extensive perturbation experiments show that ASC is robust to hand-off errors, changes in the environment layout, dynamic obstacles (e.g., people), and unexpected disturbances. Supplementary videos at adaptiveskillcoordination.github.io.

翻译：本文提出自适应技能协调（ASC）——一种实现长时域任务（如移动拾取与放置，即导航至目标物体、拾取该物体、导航至另一位置并放置该物体）的方法。ASC包含三个组成部分：（1）基础视觉运动技能库（导航、拾取、放置）；（2）技能协调策略，用于选择各时刻应执行的技能；（3）纠偏策略，用于在分布外状态下调整预训练技能。ASC的所有组件仅依赖机载视觉与本体感知，无需详细障碍物布局地图或精确物体位置等先验信息，从而简化了实际部署。我们在模拟室内环境中训练ASC，并在波士顿动力Spot机器人上以零样本方式（无任何真实环境经验或微调）部署至8个新型真实环境（1套公寓、1间实验室、2个微型厨房、2间休息室、1个办公空间、1个户外庭院）。在2个环境中的严格定量对比中，ASC实现了近乎完美的性能（59/60个回合，即98%），而顺序执行技能仅在44/60个回合（73%）中成功。大量扰动实验表明，ASC对交接误差、环境布局变化、动态障碍物（如行人）及意外干扰具有鲁棒性。补充视频见adaptiveskillcoordination.github.io。