  Advances in robotic skill acquisition have made it possible to build
general-purpose libraries of learned skills for downstream manipulation tasks.
However, naively executing these skills one after the other is unlikely to
succeed without accounting for dependencies between actions prevalent in
long-horizon plans. We present Sequencing Task-Agnostic Policies (STAP), a
scalable framework for training manipulation skills and coordinating their
geometric dependencies at planning time to solve long-horizon tasks never seen
by any skill during training. Given that Q-functions encode a measure of skill
feasibility, we formulate an optimization problem to maximize the joint success
of all skills sequenced in a plan, which we estimate by the product of their
Q-values. Our experiments indicate that this objective function approximates
ground truth plan feasibility and, when used as a planning objective, reduces
myopic behavior and thereby promotes long-horizon task success. We further
demonstrate how STAP can be used for task and motion planning by estimating the
geometric feasibility of skill sequences provided by a task planner. We
evaluate our approach in simulation and on a real robot. Qualitative results
and code are made available at https://sites.google.com/stanford.edu/stap.
