Robot learning research suggests prioritizing system identification over broad randomization

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 →

• arXiv
• Yash Tomar V

• paper
• other

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