# Discovering State and Action Abstractions for Generalized Task and Motion Planning

arXiv: https://arxiv.org/abs/2109.11082 · HTML: https://mlsanity.com/paper/2109.11082/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2021.

## Built on by (most cited first, 18 of 18)

- [Text2Motion: From Natural Language Instructions to Feasible Plans](https://mlsanity.com/paper/2303.12153/) (2023)
- [Learning Neuro-Symbolic Skills for Bilevel Planning](https://mlsanity.com/paper/2206.10680/) (2022)
- [Predicate Invention for Bilevel Planning](https://mlsanity.com/paper/2203.09634/) (2022)
- [PoE-World: Compositional World Modeling with Products of Programmatic Experts](https://mlsanity.com/paper/2505.10819/) (2025)
- [Learning Efficient Abstract Planning Models that Choose What to Predict](https://mlsanity.com/paper/2208.07737/) (2022)
- [Embodied Lifelong Learning for Task and Motion Planning](https://mlsanity.com/paper/2307.06870/) (2023)
- [Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness](https://mlsanity.com/paper/2403.10454/) (2024)
- [From Pixels to Predicates: Learning Symbolic World Models via Pretrained Vision-Language Models](https://mlsanity.com/paper/2501.00296/) (2024)
- [Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures](https://mlsanity.com/paper/2306.05171/) (2023)
- [Relational decomposition for program synthesis](https://mlsanity.com/paper/2408.12212/) (2024)
- [Neuro-Symbolic Imitation Learning: Discovering Symbolic Abstractions for Skill Learning](https://mlsanity.com/paper/2503.21406/) (2025)
- [Compose by Focus: Scene Graph-based Atomic Skills](https://mlsanity.com/paper/2509.16053/) (2025)
- [Cognitive maps are generative programs](https://mlsanity.com/paper/2504.20628/) (2025)
- [Few-Shot Neuro-Symbolic Imitation Learning for Long-Horizon Planning and Acting](https://mlsanity.com/paper/2508.21501/) (2025)
- [Coding Agents for Generalized Task and Motion Planning Problems](https://mlsanity.com/paper/2609.30233/) (2026)
- [Optimistic Reinforcement Learning-Based Skill Insertions for Task and Motion Planning](https://mlsanity.com/paper/2510.14065/) (2025)
- [Build on Priors: Vision--Language--Guided Neuro-Symbolic Imitation Learning for Data-Efficient Real-World Robot Manipulation](https://mlsanity.com/paper/2604.03759/) (2026)
- [Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning](https://mlsanity.com/paper/2605.15975/) (2026)

## Abstract (arXiv)

Generalized planning accelerates classical planning by finding an algorithm-like policy that solves multiple instances of a task. A generalized plan can be learned from a few training examples and applied to an entire domain of problems. Generalized planning approaches perform well in discrete AI planning problems that involve large numbers of objects and extended action sequences to achieve the goal. In this paper, we propose an algorithm for learning features, abstractions, and generalized plans for continuous robotic task and motion planning (TAMP) and examine the unique difficulties that arise when forced to consider geometric and physical constraints as a part of the generalized plan. Additionally, we show that these simple generalized plans learned from only a handful of examples can be used to improve the search efficiency of TAMP solvers.