# Learning Symbolic Operators for Task and Motion Planning

arXiv: https://arxiv.org/abs/2103.00589 · HTML: https://mlsanity.com/paper/2103.00589/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 28 Feb 2021.

## Builds on (most cited first, 12 of 12)

- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [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)
- [Inductive logic programming at 30: a new introduction](https://mlsanity.com/paper/2008.07912/) (2020)
- [Hierarchical Planning for Long-Horizon Manipulation with Geometric and Symbolic Scene Graphs](https://mlsanity.com/paper/2012.07277/) (2020)
- [Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image](https://mlsanity.com/paper/2006.05398/) (2020)
- [Active model learning and diverse action sampling for task and motion planning](https://mlsanity.com/paper/1803.00967/) (2018)
- [Learning to guide task and motion planning using score-space representation](https://mlsanity.com/paper/1807.09962/) (2018)
- [SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning](https://mlsanity.com/paper/2012.13037/) (2020)
- [Learning Quickly to Plan Quickly Using Modular Meta-Learning](https://mlsanity.com/paper/1809.07878/) (2018)
- [Learning STRIPS Action Models with Classical Planning](https://mlsanity.com/paper/1903.01153/) (2019)
- [Learning sparse relational transition models](https://mlsanity.com/paper/1810.11177/) (2018)

## Built on by (most cited first, 30 of 58)

- [ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation](https://mlsanity.com/paper/2409.01652/) (2024)
- [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)
- [World Model for Robot Learning: A Comprehensive Survey](https://mlsanity.com/paper/2605.00080/) (2026)
- [Learning Neuro-Symbolic Relational Transition Models for Bilevel Planning](https://mlsanity.com/paper/2105.14074/) (2021)
- [VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning](https://mlsanity.com/paper/2410.23156/) (2024)
- [Learning adaptive planning representations with natural language guidance](https://mlsanity.com/paper/2312.08566/) (2023)
- [LEAGUE: Guided Skill Learning and Abstraction for Long-Horizon Manipulation](https://mlsanity.com/paper/2210.12631/) (2022)
- [Vision-Language Interpreter for Robot Task Planning](https://mlsanity.com/paper/2311.00967/) (2023)
- [Human-in-the-Loop Task and Motion Planning for Imitation Learning](https://mlsanity.com/paper/2310.16014/) (2023)
- [PoE-World: Compositional World Modeling with Products of Programmatic Experts](https://mlsanity.com/paper/2505.10819/) (2025)
- [Practice Makes Perfect: Planning to Learn Skill Parameter Policies](https://mlsanity.com/paper/2402.15025/) (2024)
- [A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches](https://mlsanity.com/paper/2404.02817/) (2024)
- [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)
- [Composable Part-Based Manipulation](https://mlsanity.com/paper/2405.05876/) (2024)
- [DANLI: Deliberative Agent for Following Natural Language Instructions](https://mlsanity.com/paper/2210.12485/) (2022)
- [Synthesizing world models for bilevel planning](https://mlsanity.com/paper/2503.20124/) (2025)
- [LiLo-VLA: Compositional Long-Horizon Manipulation via Linked Object-Centric Policies](https://mlsanity.com/paper/2602.21531/) (2026)
- [From Pixels to Predicates: Learning Symbolic World Models via Pretrained Vision-Language Models](https://mlsanity.com/paper/2501.00296/) (2024)
- [Learning to Search in Task and Motion Planning with Streams](https://mlsanity.com/paper/2111.13144/) (2021)
- [Accelerating Integrated Task and Motion Planning with Neural Feasibility Checking](https://mlsanity.com/paper/2203.10568/) (2022)
- [ExoPredicator: Learning Abstract Models of Dynamic Worlds for Robot Planning](https://mlsanity.com/paper/2509.26255/) (2025)
- [Embodied Active Learning of Relational State Abstractions for Bilevel Planning](https://mlsanity.com/paper/2303.04912/) (2023)
- [NOD-TAMP: Generalizable Long-Horizon Planning with Neural Object Descriptors](https://mlsanity.com/paper/2311.01530/) (2023)
- [Meta-Learning Parameterized Skills](https://mlsanity.com/paper/2206.03597/) (2022)
- [PDSketch: Integrated Planning Domain Programming and Learning](https://mlsanity.com/paper/2303.05501/) (2023)
- [Creative Problem Solving in Artificially Intelligent Agents: A Survey and Framework](https://mlsanity.com/paper/2204.10358/) (2022)

## Abstract (arXiv)

Robotic planning problems in hybrid state and action spaces can be solved by integrated task and motion planners (TAMP) that handle the complex interaction between motion-level decisions and task-level plan feasibility. TAMP approaches rely on domain-specific symbolic operators to guide the task-level search, making planning efficient. In this work, we formalize and study the problem of operator learning for TAMP. Central to this study is the view that operators define a lossy abstraction of the transition model of a domain. We then propose a bottom-up relational learning method for operator learning and show how the learned operators can be used for planning in a TAMP system. Experimentally, we provide results in three domains, including long-horizon robotic planning tasks. We find our approach to substantially outperform several baselines, including three graph neural network-based model-free approaches from the recent literature. Video: https://youtu.be/iVfpX9BpBRo Code: https://git.io/JCT0g