Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Peng, Weikun"'
Autor:
Peng, Weikun, Lv, Jun, Zeng, Yuwei, Chen, Haonan, Zhao, Siheng, Sun, Jichen, Lu, Cewu, Shao, Lin
The tie-knotting task is highly challenging due to the tie's high deformation and long-horizon manipulation actions. This work presents TieBot, a Real-to-Sim-to-Real learning from visual demonstration system for the robots to learn to knot a tie. We
Externí odkaz:
http://arxiv.org/abs/2407.03245
Autor:
Xu, Zhixuan, Gao, Chongkai, Liu, Zixuan, Yang, Gang, Tie, Chenrui, Zheng, Haozhuo, Zhou, Haoyu, Peng, Weikun, Wang, Debang, Chen, Tianyi, Yu, Zhouliang, Shao, Lin
To substantially enhance robot intelligence, there is a pressing need to develop a large model that enables general-purpose robots to proficiently undertake a broad spectrum of manipulation tasks, akin to the versatile task-planning ability exhibited
Externí odkaz:
http://arxiv.org/abs/2405.06964
This work introduces a framework harnessing the capabilities of Large Language Models (LLMs) to generate primitive task conditions for generalizable long-horizon manipulations with novel objects and unseen tasks. These task conditions serve as guides
Externí odkaz:
http://arxiv.org/abs/2310.02264
Recently, learned image compression techniques have achieved remarkable performance, even surpassing the best manually designed lossy image coders. They are promising to be large-scale adopted. For the sake of practicality, a thorough investigation o
Externí odkaz:
http://arxiv.org/abs/2203.10886