AIを使用してロボットが複雑なスキルを習得 (Using AI, These Robots Learn Complicated Skills with Startling Accuracy)

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2025-01-28 カリフォルニア大学バークレー校 (UCB)

カリフォルニア大学バークレー校の研究チームは、AIを活用した新しい訓練手法により、ロボットが複雑な作業を短時間で正確に習得できるシステムを開発しました。この手法では、人間のデモンストレーションとフィードバック、さらにロボット自身の実世界での試行を組み合わせ、強化学習を通じてタスクをマスターします。例えば、「ジェンガのブロックを鞭で抜き取る」という高度な技術を、わずか1.25時間で100%の成功率で習得しました。他にも、コンピュータのマザーボードの組み立てや棚の構築など、多様なタスクを1~2時間以内に完璧にこなすことが可能です。このシステムは、ロボットが試行錯誤を通じて学習し、必要に応じて人間が介入して修正を加えることで、効率的かつ迅速に複雑なスキルを習得できる点が特徴です。

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ループ内強化学習による正確で器用なロボット操作 Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning

Jianlan Luo, Charles Xu, Jeffrey Wu, Sergey Levine
arXiv  last revised 6 Nov 2024 (this version, v2)
DOI:https://doi.org/10.48550/arXiv.2410.21845

AIを使用してロボットが複雑なスキルを習得 (Using AI, These Robots Learn Complicated Skills with Startling Accuracy)

Abstract

Reinforcement learning (RL) holds great promise for enabling autonomous acquisition of complex robotic manipulation skills, but realizing this potential in real-world settings has been challenging. We present a human-in-the-loop vision-based RL system that demonstrates impressive performance on a diverse set of dexterous manipulation tasks, including dynamic manipulation, precision assembly, and dual-arm coordination. Our approach integrates demonstrations and human corrections, efficient RL algorithms, and other system-level design choices to learn policies that achieve near-perfect success rates and fast cycle times within just 1 to 2.5 hours of training. We show that our method significantly outperforms imitation learning baselines and prior RL approaches, with an average 2x improvement in success rate and 1.8x faster execution. Through extensive experiments and analysis, we provide insights into the effectiveness of our approach, demonstrating how it learns robust, adaptive policies for both reactive and predictive control strategies. Our results suggest that RL can indeed learn a wide range of complex vision-based manipulation policies directly in the real world within practical training times. We hope this work will inspire a new generation of learned robotic manipulation techniques, benefiting both industrial applications and research advancements. Videos and code are available at our project website this https URL.

0109ロボット
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