Zobrazeno 1 - 2
of 2
pro vyhledávání: '"Shukla, Arth"'
Autor:
Tao, Stone, Xiang, Fanbo, Shukla, Arth, Qin, Yuzhe, Hinrichsen, Xander, Yuan, Xiaodi, Bao, Chen, Lin, Xinsong, Liu, Yulin, Chan, Tse-kai, Gao, Yuan, Li, Xuanlin, Mu, Tongzhou, Xiao, Nan, Gurha, Arnav, Huang, Zhiao, Calandra, Roberto, Chen, Rui, Luo, Shan, Su, Hao
Simulation has enabled unprecedented compute-scalable approaches to robot learning. However, many existing simulation frameworks typically support a narrow range of scenes/tasks and lack features critical for scaling generalizable robotics and sim2re
Externí odkaz:
http://arxiv.org/abs/2410.00425
Reinforcement learning (RL) presents a promising framework to learn policies through environment interaction, but often requires an infeasible amount of interaction data to solve complex tasks from sparse rewards. One direction includes augmenting RL
Externí odkaz:
http://arxiv.org/abs/2405.03379