# Active model learning and diverse action sampling for task and motion planning

arXiv: https://arxiv.org/abs/1803.00967 · HTML: https://mlsanity.com/paper/1803.00967/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2018.

## Built on by (most cited first, 28 of 28)

- [Text2Motion: From Natural Language Instructions to Feasible Plans](https://mlsanity.com/paper/2303.12153/) (2023)
- [Plex: Towards Reliability using Pretrained Large Model Extensions](https://mlsanity.com/paper/2207.07411/) (2022)
- [Generative Skill Chaining: Long-Horizon Skill Planning with Diffusion Models](https://mlsanity.com/paper/2401.03360/) (2023)
- [The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AI](https://mlsanity.com/paper/2103.14025/) (2021)
- [LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning](https://mlsanity.com/paper/2403.11552/) (2024)
- [QuestBench: Can LLMs ask the right question to acquire information in reasoning tasks?](https://mlsanity.com/paper/2503.22674/) (2025)
- [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)
- [LEAGUE: Guided Skill Learning and Abstraction for Long-Horizon Manipulation](https://mlsanity.com/paper/2210.12631/) (2022)
- [Proactive Agents for Multi-Turn Text-to-Image Generation Under Uncertainty](https://mlsanity.com/paper/2412.06771/) (2024)
- [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)
- [STAP: Sequencing Task-Agnostic Policies](https://mlsanity.com/paper/2210.12250/) (2022)
- [Embodied Lifelong Learning for Task and Motion Planning](https://mlsanity.com/paper/2307.06870/) (2023)
- [Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Design](https://mlsanity.com/paper/2405.02449/) (2024)
- [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)
- [Active Learning of Abstract Plan Feasibility](https://mlsanity.com/paper/2107.00683/) (2021)
- [Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures](https://mlsanity.com/paper/2306.05171/) (2023)
- [Nonmyopic Multiclass Active Search with Diminishing Returns for Diverse Discovery](https://mlsanity.com/paper/2202.03593/) (2022)
- [Closed Loop Interactive Embodied Reasoning for Robot Manipulation](https://mlsanity.com/paper/2404.15194/) (2024)
- [Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation](https://mlsanity.com/paper/2501.01367/) (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)
- [Neuro-Symbolic Skill Discovery for Conditional Multi-Level Planning](https://mlsanity.com/paper/2410.10045/) (2024)
- [An Anytime Hierarchical Approach for Stochastic Task and Motion Planning](https://mlsanity.com/paper/2108.12537/) (2021)
- [Pure Planning to Pure Policies and In Between with a Recursive Tree Planner](https://mlsanity.com/paper/2405.13130/) (2024)
- [Macro-Operator Generation and Predicate Selection for TAMP Operator Learning](https://mlsanity.com/paper/2608.23629/) (2026)

## Abstract (arXiv)

The objective of this work is to augment the basic abilities of a robot by learning to use new sensorimotor primitives to enable the solution of complex long-horizon problems. Solving long-horizon problems in complex domains requires flexible generative planning that can combine primitive abilities in novel combinations to solve problems as they arise in the world. In order to plan to combine primitive actions, we must have models of the preconditions and effects of those actions: under what circumstances will executing this primitive achieve some particular effect in the world? We use, and develop novel improvements on, state-of-the-art methods for active learning and sampling. We use Gaussian process methods for learning the conditions of operator effectiveness from small numbers of expensive training examples collected by experimentation on a robot. We develop adaptive sampling methods for generating diverse elements of continuous sets (such as robot configurations and object poses) during planning for solving a new task, so that planning is as efficient as possible. We demonstrate these methods in an integrated system, combining newly learned models with an efficient continuous-space robot task and motion planner to learn to solve long horizon problems more efficiently than was previously possible.