mlsanityData to 14 Oct 2026

Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World

arXiv 2308.07741 ↗First posted 15 Aug 2023cs.RO cs.LGcited by 3

On the map: Offline RL and imitation learning from data → Real-world robot offline RL

Abstract (arXiv)

Experimentation on real robots is demanding in terms of time and costs. For this reason, a large part of the reinforcement learning (RL) community uses simulators to develop and benchmark algorithms. However, insights gained in simulation do not necessarily translate to real robots, in particular for tasks involving complex interactions with the environment. The Real Robot Challenge 2022 therefore served as a bridge between the RL and robotics communities by allowing participants to experiment remotely with a real robot - as easily as in simulation. In the last years, offline reinforcement learning has matured into a promising paradigm for learning from pre-collected datasets, alleviating the reliance on expensive online interactions. We therefore asked the participants to learn two dexterous manipulation tasks involving pushing, grasping, and in-hand orientation from provided real-robot datasets. An extensive software documentation and an initial stage based on a simulation of the real set-up made the competition particularly accessible. By giving each team plenty of access budget to evaluate their offline-learned policies on a cluster of seven identical real TriFinger platforms, we organized an exciting competition for machine learners and roboticists alike. In this work we state the rules of the competition, present the methods used by the winning teams and compare their results with a benchmark of state-of-the-art offline RL algorithms on the challenge datasets.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Real-world robot offline RL, one of 13 directions in Offline RL and imitation learning from data. Builds on 23 of its 34 references that are in the map. First posted on arXiv 15 Aug 2023.

Builds on

23 of 34 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Benchmarking Offline Reinforcement Learning on Real-Robot Hardware28 Jul
  2. 2021What Matters in Learning from Offline Human Demonstrations for Robot Manipulation6 Aug

Other references · 21

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  3. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,809
  4. 2020D4RL: Datasets for Deep Data-Driven Reinforcement Learningcited by 1,427
  5. 2021Offline Reinforcement Learning with Implicit Q-Learningcited by 1,293
  6. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  7. 2021A Minimalist Approach to Offline Reinforcement Learningcited by 899
  8. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 743
  9. 2019Solving Rubik's Cube with a Robot Handcited by 734
  10. 2020AWAC: Accelerating Online Reinforcement Learning with Offline Datasetscited by 671
  11. 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningcited by 512
  12. 2018Behavioral Cloning from Observationcited by 492
9 more
  1. 2020Critic Regularized Regressioncited by 275
  2. 2022A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problemscited by 231
  3. 2021d3rlpy: An Offline Deep Reinforcement Learning Librarycited by 84
  4. 2020An empirical investigation of the challenges of real-world reinforcement learningcited by 77
  5. 2020Regularizing Action Policies for Smooth Control with Reinforcement Learningcited by 59
  6. 2021Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFingercited by 38
  7. 2020TriFinger: An Open-Source Robot for Learning Dexteritycited by 31
  8. 2022MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learnedcited by 26
  9. 2021Real Robot Challenge: A Robotics Competition in the Cloudcited by 14

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 2.

3 papers.

  1. 2024Dataset Clustering for Improved Offline Policy Learningcited by 1
  2. 2026TREX: Trajectory Explanations for Multi-Objective Reinforcement Learningcited by 0
  3. 2026Pareto Q-Learning with Reward Machinescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Benchmarking Reinforcement Learning Methods for Dexterous Robotic Manipulation with a Three-Fingered Gripper
  2. 2025Dexterous Grasping with Real-World Robotic Reinforcement Learning
  3. 2022Real World Offline Reinforcement Learning with Realistic Data Source
  4. 2026RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills ↗
  5. 2024Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning
  6. 2025Dexterous Manipulation through Imitation Learning: A Survey
  7. 2024Survey of Learning-based Approaches for Robotic In-Hand Manipulation
  8. 2025Real-world Reinforcement Learning from Suboptimal Interventions