# Contextual Imagined Goals for Self-Supervised Robotic Learning

arXiv: https://arxiv.org/abs/1910.11670 · HTML: https://mlsanity.com/paper/1910.11670/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2019.

## Built on by (most cited first, 30 of 40)

- [Language Conditioned Imitation Learning over Unstructured Data](https://mlsanity.com/paper/2005.07648/) (2020)
- [GNM: A General Navigation Model to Drive Any Robot](https://mlsanity.com/paper/2210.03370/) (2022)
- [Goal-Conditioned Reinforcement Learning: Problems and Solutions](https://mlsanity.com/paper/2201.08299/) (2022)
- [Rapid Exploration for Open-World Navigation with Latent Goal Models](https://mlsanity.com/paper/2104.05859/) (2021)
- [GenHowTo: Learning to Generate Actions and State Transformations from Instructional Videos](https://mlsanity.com/paper/2312.07322/) (2023)
- [Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2204.13060/) (2022)
- [What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL?](https://mlsanity.com/paper/2305.18882/) (2023)
- [Contrastive Difference Predictive Coding](https://mlsanity.com/paper/2310.20141/) (2023)
- [Robotic Offline RL from Internet Videos via Value-Function Pre-Training](https://mlsanity.com/paper/2309.13041/) (2023)
- [Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space](https://mlsanity.com/paper/2205.08129/) (2022)
- [Generalization with Lossy Affordances: Leveraging Broad Offline Data for Learning Visuomotor Tasks](https://mlsanity.com/paper/2210.06601/) (2022)
- [What Can I Do Here? Learning New Skills by Imagining Visual Affordances](https://mlsanity.com/paper/2106.00671/) (2021)
- [Self-Supervised Disentangled Representation Learning for Third-Person Imitation Learning](https://mlsanity.com/paper/2108.01069/) (2021)
- [Solving Compositional Reinforcement Learning Problems via Task Reduction](https://mlsanity.com/paper/2103.07607/) (2021)
- [Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning](https://mlsanity.com/paper/2104.05043/) (2021)
- [Understanding the Complexity Gains of Single-Task RL with a Curriculum](https://mlsanity.com/paper/2212.12809/) (2022)
- [Self-supervised Reinforcement Learning with Independently Controllable Subgoals](https://mlsanity.com/paper/2109.04150/) (2021)
- [Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning](https://mlsanity.com/paper/2110.12997/) (2021)
- [Visual Affordance Prediction for Guiding Robot Exploration](https://mlsanity.com/paper/2305.17783/) (2023)
- [DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies](https://mlsanity.com/paper/2104.11707/) (2021)
- [Continual Auxiliary Task Learning](https://mlsanity.com/paper/2202.11133/) (2022)
- [ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning](https://mlsanity.com/paper/2202.02465/) (2022)
- [Pre-trained Visual Dynamics Representations for Efficient Policy Learning](https://mlsanity.com/paper/2411.03169/) (2024)
- [Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges](https://mlsanity.com/paper/2512.04770/) (2025)
- [A Study on Dense and Sparse (Visual) Rewards in Robot Policy Learning](https://mlsanity.com/paper/2108.03222/) (2021)
- [Help Me Explore: Minimal Social Interventions for Graph-Based Autotelic Agents](https://mlsanity.com/paper/2202.05129/) (2022)
- [Intrinsic Motivation in Model-based Reinforcement Learning: A Brief Review](https://mlsanity.com/paper/2301.10067/) (2023)
- [Improving Model-Based Reinforcement Learning with Internal State Representations through Self-Supervision](https://mlsanity.com/paper/2102.05599/) (2021)
- [Towards Teachable Autotelic Agents](https://mlsanity.com/paper/2105.11977/) (2021)
- [Weakly Supervised Disentangled Representation for Goal-conditioned Reinforcement Learning](https://mlsanity.com/paper/2202.13624/) (2022)

## Abstract (arXiv)

While reinforcement learning provides an appealing formalism for learning individual skills, a general-purpose robotic system must be able to master an extensive repertoire of behaviors. Instead of learning a large collection of skills individually, can we instead enable a robot to propose and practice its own behaviors automatically, learning about the affordances and behaviors that it can perform in its environment, such that it can then repurpose this knowledge once a new task is commanded by the user? In this paper, we study this question in the context of self-supervised goal-conditioned reinforcement learning. A central challenge in this learning regime is the problem of goal setting: in order to practice useful skills, the robot must be able to autonomously set goals that are feasible but diverse. When the robot's environment and available objects vary, as they do in most open-world settings, the robot must propose to itself only those goals that it can accomplish in its present setting with the objects that are at hand. Previous work only studies self-supervised goal-conditioned RL in a single-environment setting, where goal proposals come from the robot's past experience or a generative model are sufficient. In more diverse settings, this frequently leads to impossible goals and, as we show experimentally, prevents effective learning. We propose a conditional goal-setting model that aims to propose goals that are feasible from the robot's current state. We demonstrate that this enables self-supervised goal-conditioned off-policy learning with raw image observations in the real world, enabling a robot to manipulate a variety of objects and generalize to new objects that were not seen during training.