# Learning Quickly to Plan Quickly Using Modular Meta-Learning

arXiv: https://arxiv.org/abs/1809.07878 · HTML: https://mlsanity.com/paper/1809.07878/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2018.

## Built on by (most cited first, 15 of 15)

- [Learning Symbolic Operators for Task and Motion Planning](https://mlsanity.com/paper/2103.00589/) (2021)
- [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)
- [Neural Relational Inference with Fast Modular Meta-learning](https://mlsanity.com/paper/2310.07015/) (2023)
- [Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning](https://mlsanity.com/paper/1908.06769/) (2019)
- [Representation, learning, and planning algorithms for geometric task and motion planning](https://mlsanity.com/paper/2203.04605/) (2022)
- [A Comprehensive Review of AI Agents: Transforming Possibilities in Technology and Beyond](https://mlsanity.com/paper/2508.11957/) (2025)
- [Breaking Neural Network Scaling Laws with Modularity](https://mlsanity.com/paper/2409.05780/) (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)
- [Moving Forward by Moving Backward: Embedding Action Impact over Action Semantics](https://mlsanity.com/paper/2304.12289/) (2023)
- [Anticipatory Planning: Improving Long-Lived Planning by Estimating Expected Cost of Future Tasks](https://mlsanity.com/paper/2305.04692/) (2023)
- [Compositional meta-learning through probabilistic task inference](https://mlsanity.com/paper/2510.01858/) (2025)
- [Curiosity-driven Intuitive Physics Learning](https://mlsanity.com/paper/2105.07426/) (2021)
- [Human2Any: Human-to-Robot Transfer via Constraint-Aware Compositional Planning](https://mlsanity.com/paper/2606.28813/) (2026)
- [Macro-Operator Generation and Predicate Selection for TAMP Operator Learning](https://mlsanity.com/paper/2608.23629/) (2026)

## Abstract (arXiv)

Multi-object manipulation problems in continuous state and action spaces can be solved by planners that search over sampled values for the continuous parameters of operators. The efficiency of these planners depends critically on the effectiveness of the samplers used, but effective sampling in turn depends on details of the robot, environment, and task. Our strategy is to learn functions called "specializers" that generate values for continuous operator parameters, given a state description and values for the discrete parameters. Rather than trying to learn a single specializer for each operator from large amounts of data on a single task, we take a modular meta-learning approach. We train on multiple tasks and learn a variety of specializers that, on a new task, can be quickly adapted using relatively little data -- thus, our system "learns quickly to plan quickly" using these specializers. We validate our approach experimentally in simulated 3D pick-and-place tasks with continuous state and action spaces. Visit http://tinyurl.com/chitnis-icra-19 for a supplementary video.