Benchmarking Offline Reinforcement Learning on Real-Robot Hardware
This work addresses the problem of enabling offline learning for robotics tasks, particularly dexterous manipulation, by providing a community benchmark, though it is incremental as it builds on existing methods.
The authors tackled the challenge of applying offline reinforcement learning to real-world robotics by creating a benchmark with large datasets from a dexterous manipulation platform and evaluating existing algorithms, providing a reproducible setup for future research.
Learning policies from previously recorded data is a promising direction for real-world robotics tasks, as online learning is often infeasible. Dexterous manipulation in particular remains an open problem in its general form. The combination of offline reinforcement learning with large diverse datasets, however, has the potential to lead to a breakthrough in this challenging domain analogously to the rapid progress made in supervised learning in recent years. To coordinate the efforts of the research community toward tackling this problem, we propose a benchmark including: i) a large collection of data for offline learning from a dexterous manipulation platform on two tasks, obtained with capable RL agents trained in simulation; ii) the option to execute learned policies on a real-world robotic system and a simulation for efficient debugging. We evaluate prominent open-sourced offline reinforcement learning algorithms on the datasets and provide a reproducible experimental setup for offline reinforcement learning on real systems.