FetchMan: Learning Visual Humanoid Loco-Manipulation Policies from Simulated Experiences
技术动态
来源：arXiv cs.RO发布时间待核实
arXiv:2608.17027v2 Announce Type: replace Abstract: Visual loco-manipulation policies that can generalize to novel scenes and objects have long been a goal of robotics research. However, today's data-hungry algorithms make collecting sufficient demonstrations a struggle for tabletop manipulation, and even more so for humanoids that must also walk and balance.