새로 나온 로봇, 피지컬 AI 논문과, 각 논문이 업계에 갖는 의미.
Real-world robot learning is constrained by the cost of collecting experience and evaluating candidate behaviors. Video generation models offer a scalable foundation for visual simulators that predict action outcomes before physical execution.
We study robotic in-context learning (ICL), an emerging paradigm that enables robots to infer and execute tasks from visual demonstrations. Despite its growing promise, the problem itself remains under-defined: a visual demonstration simultaneously conveys action trajectories, object semantics, manipulation affordances, spatial relations, and task goals, making it unclear what information the robot is actually expected to follow.
Encore는 몇 개의 시연을 키프레임과 그리퍼 이벤트 같은 증거 패킷으로 압축하고 코딩 에이전트가 정책 프로그램을 시뮬레이션과 실물 양팔 로봇에서 반복 수정하게 하는 프레임워크다. LIBERO-PRO의 60개 변형 셀에서 시연 3개를 쓴 프로그램이 96.3%, 시연 없이 쓴 프로그램이 94.9%의 성공률을 보였고, 실물 로봇 두 과제에서는 각각 9/10을 기록했다. 아직 동료 심사를 거치지 않은 프리프린트다.
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.
논문은 arXiv 로봇 피드에서 가져옵니다. 요약이 아직 작성되지 않은 논문은 초록의 첫 부분을 보여줍니다.