Contextual Affordances for Safe Exploration in Robotic Scenarios
Not on the map. Why?
Abstract (arXiv)
Robotics has been a popular field of research in the past few decades, with much success in industrial applications such as manufacturing and logistics. This success is led by clearly defined use cases and controlled operating environments. However, robotics has yet to make a large impact in domestic settings. This is due in part to the difficulty and complexity of designing mass-manufactured robots that can succeed in the variety of homes and environments that humans live in and that can operate safely in close proximity to humans. This paper explores the use of contextual affordances to enable safe exploration and learning in robotic scenarios targeted in the home. In particular, we propose a simple state representation that allows us to extend contextual affordances to larger state spaces and showcase how affordances can improve the success and convergence rate of a reinforcement learning algorithm in simulation. Our results suggest that after further iterations, it is possible to consider the implementation of this approach in a real robot manipulator. Furthermore, in the long term, this work could be the foundation for future explorations of human-robot interactions in complex domestic environments. This could be possible once state-of-the-art robot manipulators achieve the required level of dexterity for the described affordances in this paper.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 23 references that are in the map. First posted on arXiv 10 May 2024.
Builds on
7 of 23 references.
References · 7
- 2017Attention Is All You Needcited by 60,361
- 2019Emergent Tool Use From Multi-Agent Autocurriculacited by 313
- 2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 213
- 2019Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?cited by 140
- 2023World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challengescited by 36
- 2020Affordances in Robotic Tasks -- A Surveycited by 28
- 2023Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspectivecited by 10
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025Safe Learning for Contact-Rich Robot Tasks: A Survey from Classical Learning-Based Methods to Safe Foundation Models
- 2023Affordances from Human Videos as a Versatile Representation for Robotics
- 2025Manipulate-to-Navigate: Reinforcement Learning with Visual Affordances and Manipulability Priors
- 2024Safe Reinforcement Learning on the Constraint Manifold: Theory and Applications
- 2026In-Context Learning for Robots: Methods and Applications
- 2024Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning
- 2025Reinforcement Learning for Robotic Safe Control with Force Sensing
- 2025Long-Term Planning Around Humans in Domestic Environments with 3D Scene Graphs