# Relational Learning for Skill Preconditions

arXiv: https://arxiv.org/abs/2012.01693 · HTML: https://mlsanity.com/paper/2012.01693/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [StructDiffusion: Language-Guided Creation of Physically-Valid Structures using Unseen Objects](https://mlsanity.com/paper/2211.04604/) (2022)
- [SORNet: Spatial Object-Centric Representations for Sequential Manipulation](https://mlsanity.com/paper/2109.03891/) (2021)
- [Predicting Stable Configurations for Semantic Placement of Novel Objects](https://mlsanity.com/paper/2108.12062/) (2021)
- [Out of Sight, Still in Mind: Reasoning and Planning about Unobserved Objects with Video Tracking Enabled Memory Models](https://mlsanity.com/paper/2309.15278/) (2023)
- [Learning Action Conditions from Instructional Manuals for Instruction Understanding](https://mlsanity.com/paper/2205.12420/) (2022)
- [ELITE: Experiential Learning and Intent-Aware Transfer for Self-improving Embodied Agents](https://mlsanity.com/paper/2603.24018/) (2026)
- [Using Memory-Based Learning to Solve Tasks with State-Action Constraints](https://mlsanity.com/paper/2303.04327/) (2023)
- [StageCraft: Execution Aware Mitigation of Distractor and Obstruction Failures in VLA Models](https://mlsanity.com/paper/2603.20659/) (2026)

## Abstract (arXiv)

To determine if a skill can be executed in any given environment, a robot needs to learn the preconditions for the skill. As robots begin to operate in dynamic and unstructured environments, precondition models will need to generalize to variable number of objects with different shapes and sizes. In this work, we focus on learning precondition models for manipulation skills in unconstrained environments. Our work is motivated by the intuition that many complex manipulation tasks, with multiple objects, can be simplified by focusing on less complex pairwise object relations. We propose an object-relation model that learns continuous representations for these pairwise object relations. Our object-relation model is trained completely in simulation, and once learned, is used by a separate precondition model to predict skill preconditions for real world tasks. We evaluate our precondition model on 3 different manipulation tasks: sweeping, cutting, and unstacking. We show that our approach leads to significant improvements in predicting preconditions for all 3 tasks, across objects of different shapes and sizes.