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Robot learning research suggests prioritizing system identification over broad randomization

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the optimal allocation of resources for simulation-to-reality transfer in robot learning. The study suggests that dedicating a small portion of real-world measurement time to system identification is more effective than broad domain randomization. The findings indicate that even a limited amount of real data for parameter estimation significantly improves policy performance, outperforming policies trained with wider randomization ranges that encompass the true system parameters. AI

IMPACT Suggests a more efficient approach to sim-to-real transfer, potentially reducing real-world data collection needs for robotic systems.

RANK_REASON Research paper published on arXiv detailing findings in robot learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Robot learning research suggests prioritizing system identification over broad randomization

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Research paper published on arXiv detailing findings in robot learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Syed Hamzah Rizvi, Yash Vardhan Tomar ·

    How Should a Simulation-to-Reality Transfer Budget Be Spent?

    arXiv:2606.22062v2 Announce Type: replace-cross Abstract: Simulation-to-reality transfer, often called sim-to-real transfer, is a central challenge in robot learning. Yet, the tradeoff between measuring a system more accurately and training over a broader range of simulated dynam…

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