# Offline Learning of Counterfactual Predictions for Real-World Robotic Reinforcement Learning

arXiv: https://arxiv.org/abs/2011.05857 · HTML: https://mlsanity.com/paper/2011.05857/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [GVFs in the Real World: Making Predictions Online for Water Treatment](https://mlsanity.com/paper/2312.01624/) (2023)
- [Environment Transformer and Policy Optimization for Model-Based Offline Reinforcement Learning](https://mlsanity.com/paper/2303.03811/) (2023)
- [Build generally reusable agent-environment interaction models](https://mlsanity.com/paper/2211.08234/) (2022)

## Abstract (arXiv)

We consider real-world reinforcement learning (RL) of robotic manipulation tasks that involve both visuomotor skills and contact-rich skills. We aim to train a policy that maps multimodal sensory observations (vision and force) to a manipulator's joint velocities under practical considerations. We propose to use offline samples to learn a set of general value functions (GVFs) that make counterfactual predictions from the visual inputs. We show that combining the offline learned counterfactual predictions with force feedbacks in online policy learning allows efficient reinforcement learning given only a terminal (success/failure) reward. We argue that the learned counterfactual predictions form a compact and informative representation that enables sample efficiency and provides auxiliary reward signals that guide online explorations towards contact-rich states. Various experiments in simulation and real-world settings were performed for evaluation. Recordings of the real-world robot training can be found via https://sites.google.com/view/realrl.