# Learning Reusable Manipulation Strategies

arXiv: https://arxiv.org/abs/2311.03293 · HTML: https://mlsanity.com/paper/2311.03293/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 6 Nov 2023.

## Builds on (most cited first, 11 of 11)

- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Integrated Task and Motion Planning](https://mlsanity.com/paper/2010.01083/) (2020)
- [Where2Act: From Pixels to Actions for Articulated 3D Objects](https://mlsanity.com/paper/2101.02692/) (2021)
- [PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning](https://mlsanity.com/paper/1802.08705/) (2018)
- [Learning Task-Oriented Grasping for Tool Manipulation from Simulated Self-Supervision](https://mlsanity.com/paper/1806.09266/) (2018)
- [Global Planning for Contact-Rich Manipulation via Local Smoothing of Quasi-dynamic Contact Models](https://mlsanity.com/paper/2206.10787/) (2022)
- [The Functional Correspondence Problem](https://mlsanity.com/paper/2109.01097/) (2021)
- [Contact Mode Guided Motion Planning for Quasidynamic Dexterous Manipulation in 3D](https://mlsanity.com/paper/2105.14431/) (2021)
- [Contact Mode Guided Sampling-Based Planning for Quasistatic Dexterous Manipulation in 2D](https://mlsanity.com/paper/2011.01454/) (2020)
- [Contact-Implicit Trajectory Optimization for Dynamic Object Manipulation](https://mlsanity.com/paper/2103.01104/) (2021)
- [IFR-Explore: Learning Inter-object Functional Relationships in 3D Indoor Scenes](https://mlsanity.com/paper/2112.05298/) (2021)

## Built on by (most cited first, 9 of 9)

- [Agent Workflow Memory](https://mlsanity.com/paper/2409.07429/) (2024)
- [Yell At Your Robot: Improving On-the-Fly from Language Corrections](https://mlsanity.com/paper/2403.12910/) (2024)
- [RoboEXP: Action-Conditioned Scene Graph via Interactive Exploration for Robotic Manipulation](https://mlsanity.com/paper/2402.15487/) (2024)
- [One-Shot Manipulation Strategy Learning by Making Contact Analogies](https://mlsanity.com/paper/2411.09627/) (2024)
- [Compose by Focus: Scene Graph-based Atomic Skills](https://mlsanity.com/paper/2509.16053/) (2025)
- [Robotic Imitation of Human Actions](https://mlsanity.com/paper/2401.08381/) (2024)
- [StageGuard: Learning Stage Transitions for Long-Horizon Robot Tasks via Agentic Distillation](https://mlsanity.com/paper/2609.20791/) (2026)
- [Optimistic Reinforcement Learning-Based Skill Insertions for Task and Motion Planning](https://mlsanity.com/paper/2510.14065/) (2025)
- [Creative Robot Tool Use by Counterfactual Reasoning](https://mlsanity.com/paper/2605.05411/) (2026)

## Abstract (arXiv)

Humans demonstrate an impressive ability to acquire and generalize manipulation "tricks." Even from a single demonstration, such as using soup ladles to reach for distant objects, we can apply this skill to new scenarios involving different object positions, sizes, and categories (e.g., forks and hammers). Additionally, we can flexibly combine various skills to devise long-term plans. In this paper, we present a framework that enables machines to acquire such manipulation skills, referred to as "mechanisms," through a single demonstration and self-play. Our key insight lies in interpreting each demonstration as a sequence of changes in robot-object and object-object contact modes, which provides a scaffold for learning detailed samplers for continuous parameters. These learned mechanisms and samplers can be seamlessly integrated into standard task and motion planners, enabling their compositional use.