# Anytime Integrated Task and Motion Policies for Stochastic Environments

arXiv: https://arxiv.org/abs/1904.13006 · HTML: https://mlsanity.com/paper/1904.13006/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2019.

## Built on by (most cited first, 13 of 13)

- [Practice Makes Perfect: Planning to Learn Skill Parameter Policies](https://mlsanity.com/paper/2402.15025/) (2024)
- [Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness](https://mlsanity.com/paper/2403.10454/) (2024)
- [Extended Tree Search for Robot Task and Motion Planning](https://mlsanity.com/paper/2103.05456/) (2021)
- [Autonomous Capability Assessment of Sequential Decision-Making Systems in Stochastic Settings (Extended Version)](https://mlsanity.com/paper/2306.04806/) (2023)
- [Task and Motion Planning for Execution in the Real](https://mlsanity.com/paper/2406.03641/) (2024)
- [STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent](https://mlsanity.com/paper/2310.01775/) (2023)
- [Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot Planning](https://mlsanity.com/paper/2202.00907/) (2022)
- [Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems](https://mlsanity.com/paper/2204.04301/) (2022)
- [An Anytime Hierarchical Approach for Stochastic Task and Motion Planning](https://mlsanity.com/paper/2108.12537/) (2021)
- [JEDAI: A System for Skill-Aligned Explainable Robot Planning](https://mlsanity.com/paper/2111.00585/) (2021)
- [Task and Skill Planning: Hierarchical Robot Planning with Black-Box Skills](https://mlsanity.com/paper/2504.17901/) (2025)
- [Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning](https://mlsanity.com/paper/2605.15975/) (2026)
- [Virtues of Ordered Chaos: Planning with Topple Actions in Tabletop Stack Rearrangement](https://mlsanity.com/paper/2605.17815/) (2026)

## 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 unexecutable. 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 encoding agent behaviors handling 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 methods.