# Planning for Learning Object Properties

arXiv: https://arxiv.org/abs/2301.06054 · HTML: https://mlsanity.com/paper/2301.06054/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 42 references that are in the map.
- First posted on arXiv 15 Jan 2023.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [Interactive Perception: Leveraging Action in Perception and Perception in Action](https://mlsanity.com/paper/1604.03670/) (2016)
- [Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary](https://mlsanity.com/paper/1705.00154/) (2017)
- [Reasoning About Physical Interactions with Object-Oriented Prediction and Planning](https://mlsanity.com/paper/1812.10972/) (2018)
- [The Curious Robot: Learning Visual Representations via Physical Interactions](https://mlsanity.com/paper/1604.01360/) (2016)
- [Learning First-Order Symbolic Representations for Planning from the Structure of the State Space](https://mlsanity.com/paper/1909.05546/) (2019)
- [Grounding Predicates through Actions](https://mlsanity.com/paper/2109.14718/) (2021)
- [Unsupervised Grounding of Plannable First-Order Logic Representation from Images](https://mlsanity.com/paper/1902.08093/) (2019)
- [Safe Learning of Lifted Action Models](https://mlsanity.com/paper/2107.04169/) (2021)
- [Learning STRIPS Action Models with Classical Planning](https://mlsanity.com/paper/1903.01153/) (2019)
- [Online Object-Oriented Semantic Mapping and Map Updating](https://mlsanity.com/paper/2011.06895/) (2020)
- [Online Grounding of Symbolic Planning Domains in Unknown Environments](https://mlsanity.com/paper/2112.10007/) (2021)
- [Mindful Active Learning](https://mlsanity.com/paper/1907.12003/) (2019)
- [Learning First-Order Symbolic Planning Representations That Are Grounded](https://mlsanity.com/paper/2204.11902/) (2022)

## Built on by (most cited first, 1 of 1)

- [Embodied Active Learning of Relational State Abstractions for Bilevel Planning](https://mlsanity.com/paper/2303.04912/) (2023)

## Abstract (arXiv)

Autonomous agents embedded in a physical environment need the ability to recognize objects and their properties from sensory data. Such a perceptual ability is often implemented by supervised machine learning models, which are pre-trained using a set of labelled data. In real-world, open-ended deployments, however, it is unrealistic to assume to have a pre-trained model for all possible environments. Therefore, agents need to dynamically learn/adapt/extend their perceptual abilities online, in an autonomous way, by exploring and interacting with the environment where they operate. This paper describes a way to do so, by exploiting symbolic planning. Specifically, we formalize the problem of automatically training a neural network to recognize object properties as a symbolic planning problem (using PDDL). We use planning techniques to produce a strategy for automating the training dataset creation and the learning process. Finally, we provide an experimental evaluation in both a simulated and a real environment, which shows that the proposed approach is able to successfully learn how to recognize new object properties.