Tactile Curiosity Drives Robot Interaction
日本語版は未提供のため、英語原文で表示しています。
要旨より
Mastering robot manipulation skills via reinforcement learning (RL) remains largely sample-inefficient. The most common RL algorithms rely on random action sampling to discover new strategies, resulting in agents that allocate most of their training budget to motions in free space, away from the contacts from which manipulation skills emerge.
要旨より。当社による要約は作成中です。