Zobrazeno 1 - 10
of 90
pro vyhledávání: '"Zhang, Tingnan"'
Autor:
Feng, Yuming, Hong, Chuye, Niu, Yaru, Liu, Shiqi, Yang, Yuxiang, Yu, Wenhao, Zhang, Tingnan, Tan, Jie, Zhao, Ding
Recently, quadrupedal locomotion has achieved significant success, but their manipulation capabilities, particularly in handling large objects, remain limited, restricting their usefulness in demanding real-world applications such as search and rescu
Externí odkaz:
http://arxiv.org/abs/2411.07104
Autor:
Ma, Yecheng Jason, Hejna, Joey, Wahid, Ayzaan, Fu, Chuyuan, Shah, Dhruv, Liang, Jacky, Xu, Zhuo, Kirmani, Sean, Xu, Peng, Driess, Danny, Xiao, Ted, Tompson, Jonathan, Bastani, Osbert, Jayaraman, Dinesh, Yu, Wenhao, Zhang, Tingnan, Sadigh, Dorsa, Xia, Fei
Predicting temporal progress from visual trajectories is important for intelligent robots that can learn, adapt, and improve. However, learning such progress estimator, or temporal value function, across different tasks and domains requires both a la
Externí odkaz:
http://arxiv.org/abs/2411.04549
Navigating efficiently to an object in an unexplored environment is a critical skill for general-purpose intelligent robots. Recent approaches to this object goal navigation problem have embraced a modular strategy, integrating classical exploration
Externí odkaz:
http://arxiv.org/abs/2410.19697
Autor:
Yang, Yuxiang, Shi, Guanya, Lin, Changyi, Meng, Xiangyun, Scalise, Rosario, Castro, Mateo Guaman, Yu, Wenhao, Zhang, Tingnan, Zhao, Ding, Tan, Jie, Boots, Byron
We focus on agile, continuous, and terrain-adaptive jumping of quadrupedal robots in discontinuous terrains such as stairs and stepping stones. Unlike single-step jumping, continuous jumping requires accurately executing highly dynamic motions over l
Externí odkaz:
http://arxiv.org/abs/2409.10923
Autor:
Yao, Yihang, Cen, Zhepeng, Ding, Wenhao, Lin, Haohong, Liu, Shiqi, Zhang, Tingnan, Yu, Wenhao, Zhao, Ding
Offline safe reinforcement learning (RL) aims to train a policy that satisfies constraints using a pre-collected dataset. Most current methods struggle with the mismatch between imperfect demonstrations and the desired safe and rewarding performance.
Externí odkaz:
http://arxiv.org/abs/2407.14653
Autor:
Chiang, Hao-Tien Lewis, Xu, Zhuo, Fu, Zipeng, Jacob, Mithun George, Zhang, Tingnan, Lee, Tsang-Wei Edward, Yu, Wenhao, Schenck, Connor, Rendleman, David, Shah, Dhruv, Xia, Fei, Hsu, Jasmine, Hoech, Jonathan, Florence, Pete, Kirmani, Sean, Singh, Sumeet, Sindhwani, Vikas, Parada, Carolina, Finn, Chelsea, Xu, Peng, Levine, Sergey, Tan, Jie
An elusive goal in navigation research is to build an intelligent agent that can understand multimodal instructions including natural language and image, and perform useful navigation. To achieve this, we study a widely useful category of navigation
Externí odkaz:
http://arxiv.org/abs/2407.07775
Autor:
Lin, Changyi, Liu, Xingyu, Yang, Yuxiang, Niu, Yaru, Yu, Wenhao, Zhang, Tingnan, Tan, Jie, Boots, Byron, Zhao, Ding
Quadrupedal robots have emerged as versatile agents capable of locomoting and manipulating in complex environments. Traditional designs typically rely on the robot's inherent body parts or incorporate top-mounted arms for manipulation tasks. However,
Externí odkaz:
http://arxiv.org/abs/2403.18197
Autor:
Liang, Jacky, Xia, Fei, Yu, Wenhao, Zeng, Andy, Arenas, Montserrat Gonzalez, Attarian, Maria, Bauza, Maria, Bennice, Matthew, Bewley, Alex, Dostmohamed, Adil, Fu, Chuyuan Kelly, Gileadi, Nimrod, Giustina, Marissa, Gopalakrishnan, Keerthana, Hasenclever, Leonard, Humplik, Jan, Hsu, Jasmine, Joshi, Nikhil, Jyenis, Ben, Kew, Chase, Kirmani, Sean, Lee, Tsang-Wei Edward, Lee, Kuang-Huei, Michaely, Assaf Hurwitz, Moore, Joss, Oslund, Ken, Rao, Dushyant, Ren, Allen, Tabanpour, Baruch, Vuong, Quan, Wahid, Ayzaan, Xiao, Ted, Xu, Ying, Zhuang, Vincent, Xu, Peng, Frey, Erik, Caluwaerts, Ken, Zhang, Tingnan, Ichter, Brian, Tompson, Jonathan, Takayama, Leila, Vanhoucke, Vincent, Shafran, Izhak, Mataric, Maja, Sadigh, Dorsa, Heess, Nicolas, Rao, Kanishka, Stewart, Nik, Tan, Jie, Parada, Carolina
Large language models (LLMs) have been shown to exhibit a wide range of capabilities, such as writing robot code from language commands -- enabling non-experts to direct robot behaviors, modify them based on feedback, or compose them to perform new t
Externí odkaz:
http://arxiv.org/abs/2402.11450
Autor:
Nasiriany, Soroush, Xia, Fei, Yu, Wenhao, Xiao, Ted, Liang, Jacky, Dasgupta, Ishita, Xie, Annie, Driess, Danny, Wahid, Ayzaan, Xu, Zhuo, Vuong, Quan, Zhang, Tingnan, Lee, Tsang-Wei Edward, Lee, Kuang-Huei, Xu, Peng, Kirmani, Sean, Zhu, Yuke, Zeng, Andy, Hausman, Karol, Heess, Nicolas, Finn, Chelsea, Levine, Sergey, Ichter, Brian
Vision language models (VLMs) have shown impressive capabilities across a variety of tasks, from logical reasoning to visual understanding. This opens the door to richer interaction with the world, for example robotic control. However, VLMs produce o
Externí odkaz:
http://arxiv.org/abs/2402.07872
Online safe reinforcement learning (RL) involves training a policy that maximizes task efficiency while satisfying constraints via interacting with the environments. In this paper, our focus lies in addressing the complex challenges associated with s
Externí odkaz:
http://arxiv.org/abs/2312.15127