Learning Long-Horizon Action Dependencies in Sampling-Based Bilevel Planning

Bartłomiej Cieślar, Leslie Pack Kaelbling, Tomás Lozano-Pérez, Jorge Mendez-Mendez
Proceedings of The 8th Conference on Robot Learning, PMLR 270:4235-4252, 2025.

Abstract

Autonomous robots will need the ability to make task and motion plans that involve long sequences of actions, e.g. to prepare a meal. One challenge is that the feasibility of actions late in the plan may depend on much earlier actions. This issue is exacerbated if these dependencies exist at a purely geometric level, making them difficult to express for a task planner. Backtracking is a common technique to resolve such geometric dependencies, but its time complexity limits its applicability to short-horizon dependencies. We propose an approach to account for these dependencies by learning a search heuristic for task and motion planning. We evaluate our approach on five quasi-static simulated domains and show a substantial improvement in success rate over the baselines.

Cite this Paper

BibTeX
@InProceedings{pmlr-v270-cieslar25a, title = {Learning Long-Horizon Action Dependencies in Sampling-Based Bilevel Planning}, author = {Cie\'slar, Bart\l{}omiej and Kaelbling, Leslie Pack and Lozano-P\'erez, Tom\'as and Mendez-Mendez, Jorge}, booktitle = {Proceedings of The 8th Conference on Robot Learning}, pages = {4235--4252}, year = {2025}, editor = {Agrawal, Pulkit and Kroemer, Oliver and Burgard, Wolfram}, volume = {270}, series = {Proceedings of Machine Learning Research}, month = {06--09 Nov}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v270/main/assets/cieslar25a/cieslar25a.pdf}, url = {https://proceedings.mlr.press/v270/cieslar25a.html}, abstract = {Autonomous robots will need the ability to make task and motion plans that involve long sequences of actions, e.g. to prepare a meal. One challenge is that the feasibility of actions late in the plan may depend on much earlier actions. This issue is exacerbated if these dependencies exist at a purely geometric level, making them difficult to express for a task planner. Backtracking is a common technique to resolve such geometric dependencies, but its time complexity limits its applicability to short-horizon dependencies. We propose an approach to account for these dependencies by learning a search heuristic for task and motion planning. We evaluate our approach on five quasi-static simulated domains and show a substantial improvement in success rate over the baselines.} }
Endnote
%0 Conference Paper %T Learning Long-Horizon Action Dependencies in Sampling-Based Bilevel Planning %A Bartłomiej Cieślar %A Leslie Pack Kaelbling %A Tomás Lozano-Pérez %A Jorge Mendez-Mendez %B Proceedings of The 8th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2025 %E Pulkit Agrawal %E Oliver Kroemer %E Wolfram Burgard %F pmlr-v270-cieslar25a %I PMLR %P 4235--4252 %U https://proceedings.mlr.press/v270/cieslar25a.html %V 270 %X Autonomous robots will need the ability to make task and motion plans that involve long sequences of actions, e.g. to prepare a meal. One challenge is that the feasibility of actions late in the plan may depend on much earlier actions. This issue is exacerbated if these dependencies exist at a purely geometric level, making them difficult to express for a task planner. Backtracking is a common technique to resolve such geometric dependencies, but its time complexity limits its applicability to short-horizon dependencies. We propose an approach to account for these dependencies by learning a search heuristic for task and motion planning. We evaluate our approach on five quasi-static simulated domains and show a substantial improvement in success rate over the baselines.
APA
Cieślar, B., Kaelbling, L.P., Lozano-Pérez, T. & Mendez-Mendez, J.. (2025). Learning Long-Horizon Action Dependencies in Sampling-Based Bilevel Planning. Proceedings of The 8th Conference on Robot Learning, in Proceedings of Machine Learning Research 270:4235-4252 Available from https://proceedings.mlr.press/v270/cieslar25a.html.

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