ロボティクスとフィジカルAIの最新論文と、それぞれが産業にもたらす意味。
Encoreは、少数の遠隔操作デモを構造化エビデンスに変換し、コーディングエージェントが分析コードを書きながら政策プログラムを反復開発する枠組みである。LIBERO-PROの摂動60セルでK=3が96.3%、K=0が94.9%の封印済み成功率を達成し、実機のバイマニュアル2タスクでは各10試行中9成功した。本論文はプレプリントであり、デモがエージェントの探索を加速し戦略を伝達することを示した。
While contact-rich manipulation requires deliberate regulation of interaction forces, recent approaches to robot manipulation learning predominantly represent actions as target positions or poses. Even methods that incorporate force sensing either use it solely as an observation or, when predicting forces as part of the output, rely on a hybrid force controller.
Vision-language models can coordinate long-horizon robot manipulation, yet successful task reasoning still depends on whether local physical interactions produce the intended effects. We study how repeated interaction can improve this capability without updating the base model.
When interacting with an unfamiliar deformable material, a robot lacks prior knowledge of its physical properties and how it will respond to applied forces and motion. Rapid online identification is therefore essential for reliable manipulation.
Robotic foundation models (RFMs) are increasingly capable of general-purpose manipulation, yet reliable physical interaction remains challenging in contact-rich settings. We present FP2, a lightweight downstream interface that equips task-adapted RFMs with explicit force control while preserving their action-generation capability.
論文は arXiv のロボティクス分野から取得しています。当社の要約が未作成の場合は、要旨の冒頭を表示します。