# An Anytime Hierarchical Approach for Stochastic Task and Motion Planning

arXiv: https://arxiv.org/abs/2108.12537 · HTML: https://mlsanity.com/paper/2108.12537/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 67 references that are in the map.
- First posted on arXiv 28 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [Integrated Task and Motion Planning](https://mlsanity.com/paper/2010.01083/) (2020)
- [PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning](https://mlsanity.com/paper/1802.08705/) (2018)
- [Deep Reinforcement Learning in Parameterized Action Space](https://mlsanity.com/paper/1511.04143/) (2015)
- [Online Replanning in Belief Space for Partially Observable Task and Motion Problems](https://mlsanity.com/paper/1911.04577/) (2019)
- [Active model learning and diverse action sampling for task and motion planning](https://mlsanity.com/paper/1803.00967/) (2018)
- [Anytime Integrated Task and Motion Policies for Stochastic Environments](https://mlsanity.com/paper/1904.13006/) (2019)

## Abstract (arXiv)

In order to solve complex, long-horizon tasks, intelligent robots need to carry out high-level, abstract planning and reasoning in conjunction with motion planning. However, abstract models are typically lossy and plans or policies computed using them can be inexecutable. These problems are exacerbated in stochastic situations where the robot needs to reason about and plan for multiple contingencies. We present a new approach for integrated task and motion planning in stochastic settings. In contrast to prior work in this direction, we show that our approach can effectively compute integrated task and motion policies whose branching structures encode agent behaviors that handle multiple execution-time contingencies. We prove that our algorithm is probabilistically complete and can compute feasible solution policies in an anytime fashion so that the probability of encountering an unresolved contingency decreases over time. Empirical results on a set of challenging problems show the utility and scope of our method.