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。
PL-MPC 是 arXiv 预印本，未注明经同行评审。该方法在 TD-M(PC)2 上同步修改 critic 监督、MPC 终端值估计和 planner 到策略的转移，不改世界模型与 MPPI。在 HumanoidBench 的 balance-hard 上 TAR 从 98±18 升至 387±255，hurdle 从 199±13 升至 466±200；在 KUKA IIWA14 真机 wrench–nut 对齐中，训练尺寸和两个未见尺寸的观测成功率均高于 TD-M(PC)2。
Safe social navigation requires a robot to anticipate not only the future consequences of its actions, but also whether a nominal action can actually be executed under surrounding physical and social constraints. We present Social-WM, an efficient latent world-model planning framework trained from egocentric RGB video sequences.
RoboCoach 是一个预印本框架，用世界模型中的想象执行来诊断长程机器人任务中最先失败的子任务，并据此请求对应技能专家的演示数据。在 Franka 和 AgileX 真机上，仅追加 150 条子任务演示，成功率分别从 13.3% 升至 75.0%、从 40.0% 升至 83.8%，显著超过统一采集加共享适配器的基线。该工作表明世界模型可以成为主动教练，把稀缺的真实数据导向可复用的技能模块。
Latent world models learn action-conditioned dynamics in representation space and often score candidate actions by Euclidean distance to a goal representation. Joint training typically regularizes the representation to prevent collapse, but the resulting representation geometry also determines how terminal errors are weighted during planning.
A video world model trained on robot egocentric footage predicts outcomes of actions for evaluation and planning.
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。