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Benchmarking Offline Reinforcement Learning on Real-Robot Hardware

2023-07-28 · Nico Gürtler, Sebastian Blaes, Pavel Kolev, Felix Widmaier, Manuel Wüthrich, Stefan Bauer, Bernhard Schölkopf, Georg Martius

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.

📄 PDF Abstract BibTeX arXiv:2307.15690

Code (2)

rr-learning/trifinger-rl-example 공식 구현 pytorch

rr-learning/trifinger_rl_datasets 공식 구현

Tasks

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Solving Multi-Goal Robotic Tasks with Decision Transformer

2024-10-08 · Paul Gajewski, Dominik Żurek, Marcin Pietroń, Kamil Faber

Artificial intelligence plays a crucial role in robotics, with reinforcement learning (RL) emerging as one of the most promising approaches for robot control. However, several key challenges hinder its broader applicatio…

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A Real-World Quadrupedal Locomotion Benchmark for Offline Reinforcement Learning

2023-09-13 · Hongyin Zhang, Shuyu Yang, Donglin Wang

Online reinforcement learning (RL) methods are often data-inefficient or unreliable, making them difficult to train on real robotic hardware, especially quadruped robots. Learning robotic tasks from pre-collected data is…

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Train Offline, Test Online: A Real Robot Learning Benchmark

2023-06-01 · Gaoyue Zhou, Victoria Dean, Mohan Kumar Srirama, Aravind Rajeswaran 외

Three challenges limit the progress of robot learning research: robots are expensive (few labs can participate), everyone uses different robots (findings do not generalize across labs), and we lack internet-scale robotic…

Robotic Manipulation Datasets for Offline Compositional Reinforcement Learning

2023-07-13 · Marcel Hussing, Jorge A. Mendez, Anisha Singrodia, Cassandra Kent 외

Offline reinforcement learning (RL) is a promising direction that allows RL agents to pre-train on large datasets, avoiding the recurrence of expensive data collection. To advance the field, it is crucial to generate lar…

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Datasets and Benchmarks for Offline Safe Reinforcement Learning

2023-06-15 · Zuxin Liu, Zijian Guo, Haohong Lin, Yihang Yao 외

This paper presents a comprehensive benchmarking suite tailored to offline safe reinforcement learning (RL) challenges, aiming to foster progress in the development and evaluation of safe learning algorithms in both the …

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