Zobrazeno 1 - 10
of 471
pro vyhledávání: '"Lai, Hang"'
Reinforcement Learning (RL) has shown its remarkable and generalizable capability in legged locomotion through sim-to-real transfer. However, while adaptive methods like domain randomization are expected to make policy more robust to diverse environm
Externí odkaz:
http://arxiv.org/abs/2409.17992
Autor:
Lai, Hang, Cao, Jiahang, Xu, Jiafeng, Wu, Hongtao, Lin, Yunfeng, Kong, Tao, Yu, Yong, Zhang, Weinan
Legged locomotion over various terrains is challenging and requires precise perception of the robot and its surroundings from both proprioception and vision. However, learning directly from high-dimensional visual input is often data-inefficient and
Externí odkaz:
http://arxiv.org/abs/2409.16784
Quadruped robots have strong adaptability to extreme environments but may also experience faults. Once these faults occur, robots must be repaired before returning to the task, reducing their practical feasibility. One prevalent concern among these f
Externí odkaz:
http://arxiv.org/abs/2312.17606
Autor:
He, Haoran, Wu, Peilin, Bai, Chenjia, Lai, Hang, Wang, Lingxiao, Pan, Ling, Hu, Xiaolin, Zhang, Weinan
Reinforcement Learning (RL) has recently achieved remarkable success in robotic control. However, most works in RL operate in simulated environments where privileged knowledge (e.g., dynamics, surroundings, terrains) is readily available. Conversely,
Externí odkaz:
http://arxiv.org/abs/2305.18464
Robots are traditionally bounded by a fixed embodiment during their operational lifetime, which limits their ability to adapt to their surroundings. Co-optimizing control and morphology of a robot, however, is often inefficient due to the complex int
Externí odkaz:
http://arxiv.org/abs/2212.09078
Deep reinforcement learning has recently emerged as an appealing alternative for legged locomotion over multiple terrains by training a policy in physical simulation and then transferring it to the real world (i.e., sim-to-real transfer). Despite con
Externí odkaz:
http://arxiv.org/abs/2212.07740
Publikováno v:
Social Transformations in Chinese Societies, 2024, Vol. 20, Issue 1, pp. 63-72.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/STICS-09-2023-0003
Reinforcement learning (RL) solves sequential decision-making problems via a trial-and-error process interacting with the environment. While RL achieves outstanding success in playing complex video games that allow huge trial-and-error, making errors
Externí odkaz:
http://arxiv.org/abs/2206.09328
Publikováno v:
In Computer Vision and Image Understanding September 2024 246
Autor:
Lai, Hang, Shen, Jian, Zhang, Weinan, Huang, Yimin, Zhang, Xing, Tang, Ruiming, Yu, Yong, Li, Zhenguo
Model-based reinforcement learning has attracted wide attention due to its superior sample efficiency. Despite its impressive success so far, it is still unclear how to appropriately schedule the important hyperparameters to achieve adequate performa
Externí odkaz:
http://arxiv.org/abs/2111.08550