# Learning to guide task and motion planning using score-space representation

arXiv: https://arxiv.org/abs/1807.09962 · HTML: https://mlsanity.com/paper/1807.09962/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2018.

## Built on by (most cited first, 27 of 27)

- [Pre-trained Gaussian Processes for Bayesian Optimization](https://mlsanity.com/paper/2109.08215/) (2021)
- [TASKOGRAPHY: Evaluating robot task planning over large 3D scene graphs](https://mlsanity.com/paper/2207.05006/) (2022)
- [Learning Symbolic Operators for Task and Motion Planning](https://mlsanity.com/paper/2103.00589/) (2021)
- [Grammar Prompting for Domain-Specific Language Generation with Large Language Models](https://mlsanity.com/paper/2305.19234/) (2023)
- [NeRP: Neural Rearrangement Planning for Unknown Objects](https://mlsanity.com/paper/2106.01352/) (2021)
- [Efficient and Interpretable Robot Manipulation with Graph Neural Networks](https://mlsanity.com/paper/2102.13177/) (2021)
- [A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches](https://mlsanity.com/paper/2404.02817/) (2024)
- [Guided Imitation of Task and Motion Planning](https://mlsanity.com/paper/2112.03386/) (2021)
- [Generalizable Long-Horizon Manipulations with Large Language Models](https://mlsanity.com/paper/2310.02264/) (2023)
- [Reconstructing Interactive 3D Scenes by Panoptic Mapping and CAD Model Alignments](https://mlsanity.com/paper/2103.16095/) (2021)
- [Efficient Task Planning for Mobile Manipulation: a Virtual Kinematic Chain Perspective](https://mlsanity.com/paper/2108.01259/) (2021)
- [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)
- [Representation, learning, and planning algorithms for geometric task and motion planning](https://mlsanity.com/paper/2203.04605/) (2022)
- [A Survey on the Integration of Machine Learning with Sampling-based Motion Planning](https://mlsanity.com/paper/2211.08368/) (2022)
- [Pre-training helps Bayesian optimization too](https://mlsanity.com/paper/2207.03084/) (2022)
- [HyperBO+: Pre-training a universal prior for Bayesian optimization with hierarchical Gaussian processes](https://mlsanity.com/paper/2212.10538/) (2022)
- [Closed Loop Interactive Embodied Reasoning for Robot Manipulation](https://mlsanity.com/paper/2404.15194/) (2024)
- [Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search](https://mlsanity.com/paper/2506.07062/) (2025)
- [Anticipatory Planning: Improving Long-Lived Planning by Estimating Expected Cost of Future Tasks](https://mlsanity.com/paper/2305.04692/) (2023)
- [ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation](https://mlsanity.com/paper/2604.23099/) (2026)
- [Policy-Guided Lazy Search with Feedback for Task and Motion Planning](https://mlsanity.com/paper/2210.14055/) (2022)
- [RobKiNet: Robotic Kinematics Informed Neural Network for Optimal Robot Configuration Prediction](https://mlsanity.com/paper/2402.16281/) (2024)
- [Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning](https://mlsanity.com/paper/2605.15975/) (2026)
- [Learning Motion Feasibility from Point Clouds in Cluttered Environments](https://mlsanity.com/paper/2606.26700/) (2026)
- [Courteous Anticipation: Improving Long-Lived Task Planning in Persistent Shared Environments](https://mlsanity.com/paper/2607.20289/) (2026)
- [Macro-Operator Generation and Predicate Selection for TAMP Operator Learning](https://mlsanity.com/paper/2608.23629/) (2026)

## Abstract (arXiv)

In this paper, we propose a learning algorithm that speeds up the search in task and motion planning problems. Our algorithm proposes solutions to three different challenges that arise in learning to improve planning efficiency: what to predict, how to represent a planning problem instance, and how to transfer knowledge from one problem instance to another. We propose a method that predicts constraints on the search space based on a generic representation of a planning problem instance, called score-space, where we represent a problem instance in terms of the performance of a set of solutions attempted so far. Using this representation, we transfer knowledge, in the form of constraints, from previous problems based on the similarity in score space. We design a sequential algorithm that efficiently predicts these constraints, and evaluate it in three different challenging task and motion planning problems. Results indicate that our approach performs orders of magnitudes faster than an unguided planner