  Task and Motion Planning (TAMP) algorithms solve long-horizon robotics tasks
by integrating task planning with motion planning; the task planner proposes a
sequence of actions towards a goal state and the motion planner verifies
whether this action sequence is geometrically feasible for the robot. However,
state-of-the-art TAMP algorithms do not scale well with the difficulty of the
task and require an impractical amount of time to solve relatively small
problems. We propose Constraints and Streams for Task and Motion Planning
(COAST), a probabilistically-complete, sampling-based TAMP algorithm that
combines stream-based motion planning with an efficient, constrained task
planning strategy. We validate COAST on three challenging TAMP domains and
demonstrate that our method outperforms baselines in terms of cumulative task
planning time by an order of magnitude. You can find more supplementary
materials on our project
\href{https://branvu.github.io/coast.github.io}{website}.
