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COAST: Constraints and Streams for Task and Motion Planning

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arxiv 2405.08572 v1 pith:3DQ7VVZK submitted 2024-05-14 cs.RO

classification cs.RO
keywords planningtaskmotioncoasttampalgorithmsconstraintsgithub
verification ladder T0 review T1 audit T2 compute T3 formal
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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}.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Robot Operation of Home Appliances by Reading User Manuals

    cs.RO 2025-05 conditional novelty 6.0 of 10

    A robot system that constructs a symbolic appliance model from a user manual and uses it to reliably execute natural language appliance operation tasks, outperforming direct VLM-based policies.

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