Task and Motion Planning Is PSPACE-Complete

Authors: William Vega-Brown, Nicholas Roy10385-10392

AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We present a new representation for task and motion planning that uses constraints to capture both continuous and discrete phenomena in a unified framework. We show that we can decide if a feasible plan exists for a given problem instance using only polynomial space if the constraints are semialgebraic and all actions have uniform stratified accessibility, a technical condition closely related to both controllability and to the existence of a symbolic representation of a planning domain. We show that there cannot exist an algorithm that solves the more general problem of deciding if a plan exists for an instance with arbitrary semialgebraic constraints. Finally, we show that our formalism is universal, in the sense that every deterministic robotic planning problem can be well-approximated within our formalism. Together, these results imply task and motion planning is PSPACE-complete.
Researcher Affiliation Academia William Vega-Brown, Nicholas Roy CSAIL, MIT 32 Vassar Street, 32-33x Cambridge, Massachusetts 02139 {wrvb,nickroy}@csail.mit.edu
Pseudocode Yes Algorithm 1 An algorithm to enumerate reachable subsets in semialgebraic C3. Algorithm 2 An algorithm for determining plan existence.
Open Source Code No The paper does not contain any statements about releasing code, nor does it provide links to source code repositories.
Open Datasets No The paper is theoretical and does not conduct experiments on datasets, thus no dataset access information for training is provided.
Dataset Splits No The paper is theoretical and does not conduct experiments with dataset splits for training, validation, or testing.
Hardware Specification No The paper is theoretical and does not describe any specific hardware used for running experiments.
Software Dependencies No The paper is theoretical and does not list specific software dependencies with version numbers for implementation.
Experiment Setup No The paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations.