Unified Task and Motion Planning using Object-centric Abstractions of Motion Constraints
CoRR(2023)
摘要
In task and motion planning (TAMP), the ambiguity and underdetermination of
abstract descriptions used by task planning methods make it difficult to
characterize physical constraints needed to successfully execute a task. The
usual approach is to overlook such constraints at task planning level and to
implement expensive sub-symbolic geometric reasoning techniques that perform
multiple calls on unfeasible actions, plan corrections, and re-planning until a
feasible solution is found. We propose an alternative TAMP approach that
unifies task and motion planning into a single heuristic search. Our approach
is based on an object-centric abstraction of motion constraints that permits
leveraging the computational efficiency of off-the-shelf AI heuristic search to
yield physically feasible plans. These plans can be directly transformed into
object and motion parameters for task execution without the need of intensive
sub-symbolic geometric reasoning.
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