  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 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
eight novel real-world environments (one apartment, one lab, two microkitchens,
two lounges, one office space, one outdoor courtyard). In rigorous quantitative
comparisons in two 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.
