# Self-supervised Visual Reinforcement Learning with Object-centric Representations

arXiv: https://arxiv.org/abs/2011.14381 · HTML: https://mlsanity.com/paper/2011.14381/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2020.

## Built on by (most cited first, 30 of 44)

- [Bridging the Gap to Real-World Object-Centric Learning](https://mlsanity.com/paper/2209.14860/) (2022)
- [SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models](https://mlsanity.com/paper/2305.11281/) (2023)
- [Object-Centric Learning for Real-World Videos by Predicting Temporal Feature Similarities](https://mlsanity.com/paper/2306.04829/) (2023)
- [An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning](https://mlsanity.com/paper/2302.04419/) (2023)
- [Dream to Manipulate: Compositional World Models Empowering Robot Imitation Learning with Imagination](https://mlsanity.com/paper/2412.14957/) (2024)
- [Temporally Consistent Object-Centric Learning by Contrasting Slots](https://mlsanity.com/paper/2412.14295/) (2024)
- [Entity-Centric Reinforcement Learning for Object Manipulation from Pixels](https://mlsanity.com/paper/2404.01220/) (2024)
- [Policy Architectures for Compositional Generalization in Control](https://mlsanity.com/paper/2203.05960/) (2022)
- [Towards Deeper Deep Reinforcement Learning with Spectral Normalization](https://mlsanity.com/paper/2106.01151/) (2021)
- [Compositional Multi-Object Reinforcement Learning with Linear Relation Networks](https://mlsanity.com/paper/2201.13388/) (2022)
- [Zero-Shot Object-Centric Representation Learning](https://mlsanity.com/paper/2408.09162/) (2024)
- [Self-supervised Reinforcement Learning with Independently Controllable Subgoals](https://mlsanity.com/paper/2109.04150/) (2021)
- [Object-Centric World Model for Language-Guided Manipulation](https://mlsanity.com/paper/2503.06170/) (2025)
- [Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning](https://mlsanity.com/paper/2307.09205/) (2023)
- [BlendRL: A Framework for Merging Symbolic and Neural Policy Learning](https://mlsanity.com/paper/2410.11689/) (2024)
- [Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter](https://mlsanity.com/paper/2208.03374/) (2022)
- [CTRL-O: Language-Controllable Object-Centric Visual Representation Learning](https://mlsanity.com/paper/2503.21747/) (2025)
- [Unsupervised Multi-object Segmentation Using Attention and Soft-argmax](https://mlsanity.com/paper/2205.13271/) (2022)
- [Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning](https://mlsanity.com/paper/2406.03234/) (2024)
- [Learning Interactive World Model for Object-Centric Reinforcement Learning](https://mlsanity.com/paper/2511.02225/) (2025)
- [Object-Centric Representation Learning with Generative Spatial-Temporal Factorization](https://mlsanity.com/paper/2111.05393/) (2021)
- [SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions](https://mlsanity.com/paper/2410.18416/) (2024)
- [SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models](https://mlsanity.com/paper/2503.01584/) (2025)
- [Recurrent Complex-Weighted Autoencoders for Unsupervised Object Discovery](https://mlsanity.com/paper/2405.17283/) (2024)
- [EC-Diffuser: Multi-Object Manipulation via Entity-Centric Behavior Generation](https://mlsanity.com/paper/2412.18907/) (2024)
- [Hierarchical Entity-centric Reinforcement Learning with Factored Subgoal Diffusion](https://mlsanity.com/paper/2602.02722/) (2026)
- [Object-Category Aware Reinforcement Learning](https://mlsanity.com/paper/2210.07802/) (2022)
- [Object-Centric Pretraining via Target Encoder Bootstrapping](https://mlsanity.com/paper/2503.15141/) (2025)
- [OCRA: Object-Centric Learning with 3D and Tactile Priors for Human-to-Robot Action Transfer](https://mlsanity.com/paper/2603.14401/) (2026)
- [Object-Centric Temporal Consistency via Conditional Autoregressive Inductive Biases](https://mlsanity.com/paper/2410.15728/) (2024)

## Abstract (arXiv)

Autonomous agents need large repertoires of skills to act reasonably on new tasks that they have not seen before. However, acquiring these skills using only a stream of high-dimensional, unstructured, and unlabeled observations is a tricky challenge for any autonomous agent. Previous methods have used variational autoencoders to encode a scene into a low-dimensional vector that can be used as a goal for an agent to discover new skills. Nevertheless, in compositional/multi-object environments it is difficult to disentangle all the factors of variation into such a fixed-length representation of the whole scene. We propose to use object-centric representations as a modular and structured observation space, which is learned with a compositional generative world model. We show that the structure in the representations in combination with goal-conditioned attention policies helps the autonomous agent to discover and learn useful skills. These skills can be further combined to address compositional tasks like the manipulation of several different objects.