机器人与具身智能领域的最新论文，以及每篇对产业的意义。
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.
这篇arXiv预印本提出Encore框架，将遥操作演示蒸馏为编码智能体可查询的证据包，让智能体编写、运行并迭代修复策略程序，运行时不调用模型。在LIBERO-PRO的60个扰动单元上，3条演示的冻结程序在3000个密封回合中成功2889次（96.3%），无演示为2846次（94.9%）；在RoboDojo上无演示全部失败，3条演示在前10个任务取得95/450成功。双臂真机上完成手递手和杯子倒扣，各9/10成功。
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.
论文来自 arXiv 机器人领域。尚未生成摘要的论文，显示摘要开头部分。