Výsledky vyhledávání - "Shao, Yecheng"

Report

Mamba Policy: Towards Efficient 3D Diffusion Policy with Hybrid Selective State Models

Autor: Cao, Jiahang, Zhang, Qiang, Sun, Jingkai, Wang, Jiaxu, Cheng, Hao, Li, Yulin, Ma, Jun, Shao, Yecheng, Zhao, Wen, Han, Gang, Guo, Yijie, Xu, Renjing

Diffusion models have been widely employed in the field of 3D manipulation due to their efficient capability to learn distributions, allowing for precise prediction of action trajectories. However, diffusion models typically rely on large parameter U

Externí odkaz: http://arxiv.org/abs/2409.07163

Zobrazit plný text záznamu

Report

Mamba as Decision Maker: Exploring Multi-scale Sequence Modeling in Offline Reinforcement Learning

Autor: Cao, Jiahang, Zhang, Qiang, Wang, Ziqing, Sun, Jingkai, Wang, Jiaxu, Cheng, Hao, Shao, Yecheng, Zhao, Wen, Han, Gang, Guo, Yijie, Xu, Renjing

Sequential modeling has demonstrated remarkable capabilities in offline reinforcement learning (RL), with Decision Transformer (DT) being one of the most notable representatives, achieving significant success. However, RL trajectories possess unique

Externí odkaz: http://arxiv.org/abs/2406.02013

Zobrazit plný text záznamu

Report

Reinforcement Learning with Generalizable Gaussian Splatting

Autor: Wang, Jiaxu, Zhang, Qiang, Sun, Jingkai, Cao, Jiahang, Han, Gang, Zhao, Wen, Zhang, Weining, Shao, Yecheng, Guo, Yijie, Xu, Renjing

An excellent representation is crucial for reinforcement learning (RL) performance, especially in vision-based reinforcement learning tasks. The quality of the environment representation directly influences the achievement of the learning task. Previ

Externí odkaz: http://arxiv.org/abs/2404.07950

Zobrazit plný text záznamu

Report

Learning Free Gait Transition for Quadruped Robots via Phase-Guided Controller

Autor: Shao, Yecheng, Jin, Yongbin, Liu, Xianwei, He, Weiyan, Wang, Hongtao, Yang, Wei

Gaits and transitions are key components in legged locomotion. For legged robots, describing and reproducing gaits as well as transitions remain longstanding challenges. Reinforcement learning has become a powerful tool to formulate controllers for l

Externí odkaz: http://arxiv.org/abs/2201.00206

Zobrazit plný text záznamu

Report

Direct Observation of Room-Temperature Dislocation Plasticity in Diamond

Autor: Nie, Anmin, Bu, Yeqiang, Huang, Junquan, Shao, Yecheng, Zhang, Yizhi, Hu, Wentao, Liu, Jiabin, Wang, Yanbin, Xu, Bo, Liu, Zhongyuan, Wang, Hongtao, Yang, Wei, Tian, Yongjun

It is well known that diamond does not deform plastically at room temperature and usually fails in catastrophic brittle fracture. Here we demonstrate room-temperature dislocation plasticity in sub-micrometer sized diamond pillars by in-situ mechanica

Externí odkaz: http://arxiv.org/abs/2002.05913

Zobrazit plný text záznamu