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What Matters for Simulation to Online Reinforcement Learning on Real Robots
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What Matters for Simulation to Online Reinforcement Learning on Real Robots

#Reinforcement Learning #Robotics #Online Learning #Machine Learning #Simulation #Empirical Study #Design Choices #Real Robots

📌 Key Takeaways

  • Researchers identified specific design choices critical for successful online RL on physical robots
  • Study involved 100 real-world training runs across three distinct robotic platforms
  • Some commonly used RL defaults can be harmful rather than helpful
  • Certain standard RL design choices produce stable learning across tasks and hardware
  • Research provides first large-sample empirical study of these design choices

📖 Full Retelling

A team of researchers led by Yarden As and including Dhruva Tirumala, René Zurbrügg, Chenhao Li, Stelian Coros, Andreas Krause, and Markus Wulfmeier published a groundbreaking study on February 23, 2026, investigating what specific design choices enable successful online reinforcement learning on physical robots, conducted across 100 real-world training runs on three distinct robotic platforms to address the challenge of deploying RL systems with lower engineering effort. The research, submitted to the arXiv preprint server under the category Computer Science > Robotics, systematically examines algorithmic, systems, and experimental decisions that are often left implicit in prior work, providing unprecedented empirical evidence about what truly matters when transitioning from simulation to real-world robotic applications. The researchers discovered that some widely used defaults in reinforcement learning can actually be detrimental, while a specific set of robust design choices within standard RL practice consistently yield stable learning across diverse tasks and hardware configurations, offering practitioners a clear roadmap for more effective implementation.

🏷️ Themes

Robotics, Reinforcement Learning, Machine Learning

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Original Source
--> Computer Science > Robotics arXiv:2602.20220 [Submitted on 23 Feb 2026] Title: What Matters for Simulation to Online Reinforcement Learning on Real Robots Authors: Yarden As , Dhruva Tirumala , René Zurbrügg , Chenhao Li , Stelian Coros , Andreas Krause , Markus Wulfmeier View a PDF of the paper titled What Matters for Simulation to Online Reinforcement Learning on Real Robots, by Yarden As and 6 other authors View PDF HTML Abstract: We investigate what specific design choices enable successful online reinforcement learning on physical robots. Across 100 real-world training runs on three distinct robotic platforms, we systematically ablate algorithmic, systems, and experimental decisions that are typically left implicit in prior work. We find that some widely used defaults can be harmful, while a set of robust, readily adopted design choices within standard RL practice yield stable learning across tasks and hardware. These results provide the first large-sample empirical study of such design choices, enabling practitioners to deploy online RL with lower engineering effort. Subjects: Robotics (cs.RO) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2602.20220 [cs.RO] (or arXiv:2602.20220v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2602.20220 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yarden As [ view email ] [v1] Mon, 23 Feb 2026 10:34:15 UTC (17,102 KB) Full-text links: Access Paper: View a PDF of the paper titled What Matters for Simulation to Online Reinforcement Learning on Real Robots, by Yarden As and 6 other authors View PDF HTML TeX Source view license Current browse context: cs.RO < prev | next > new | recent | 2026-02 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer T...
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