Zobrazeno 1 - 10
of 3 164
pro vyhledávání: '"XIAN ZHOU"'
Autor:
Lin, Chunru, Fan, Jugang, Wang, Yian, Yang, Zeyuan, Chen, Zhehuan, Fang, Lixing, Wang, Tsun-Hsuan, Xian, Zhou, Gan, Chuang
It is desired to equip robots with the capability of interacting with various soft materials as they are ubiquitous in the real world. While physics simulations are one of the predominant methods for data collection and robot training, simulating sof
Externí odkaz:
http://arxiv.org/abs/2411.12711
Autor:
Wang, Yian, Qiu, Xiaowen, Liu, Jiageng, Chen, Zhehuan, Cai, Jiting, Wang, Yufei, Wang, Tsun-Hsuan, Xian, Zhou, Gan, Chuang
Creating large-scale interactive 3D environments is essential for the development of Robotics and Embodied AI research. Current methods, including manual design, procedural generation, diffusion-based scene generation, and large language model (LLM)
Externí odkaz:
http://arxiv.org/abs/2411.09823
In this work, we aim to teach robots to manipulate various thin-shell materials. Prior works studying thin-shell object manipulation mostly rely on heuristic policies or learn policies from real-world video demonstrations, and only focus on limited m
Externí odkaz:
http://arxiv.org/abs/2404.00451
We introduce DIFFTACTILE, a physics-based differentiable tactile simulation system designed to enhance robotic manipulation with dense and physically accurate tactile feedback. In contrast to prior tactile simulators which primarily focus on manipula
Externí odkaz:
http://arxiv.org/abs/2403.08716
Autor:
Wang, Yufei, Sun, Zhanyi, Zhang, Jesse, Xian, Zhou, Biyik, Erdem, Held, David, Erickson, Zackory
Reward engineering has long been a challenge in Reinforcement Learning (RL) research, as it often requires extensive human effort and iterative processes of trial-and-error to design effective reward functions. In this paper, we propose RL-VLM-F, a m
Externí odkaz:
http://arxiv.org/abs/2402.03681
Autor:
Wang, Yufei, Xian, Zhou, Chen, Feng, Wang, Tsun-Hsuan, Wang, Yian, Fragkiadaki, Katerina, Erickson, Zackory, Held, David, Gan, Chuang
We present RoboGen, a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation. RoboGen leverages the latest advancements in foundation and generative models. Instead of directly using or adapting t
Externí odkaz:
http://arxiv.org/abs/2311.01455
Generalist robot manipulators need to learn a wide variety of manipulation skills across diverse environments. Current robot training pipelines rely on humans to provide kinesthetic demonstrations or to program simulation environments and to code up
Externí odkaz:
http://arxiv.org/abs/2310.18308
Publikováno v:
口腔疾病防治, Vol 32, Iss 12, Pp 933-944 (2024)
Objective To analyze the trends and hotspots in research related to microbiomes and microbes of dental caries; in addition, the study seeks to provide a reference for caries research. Methods We searched the Web of Science Core Collection (WOSCC) to
Externí odkaz:
https://doaj.org/article/43df264d942b45008279d5d48429e280
Autor:
Xingxing Zhu, Yue Wu, Yanfeng Li, Xian Zhou, Jens O. Watzlawik, Yin Maggie Chen, Ariel L. Raybuck, Daniel D. Billadeau, Virginia Smith Shapiro, Wolfdieter Springer, Jie Sun, Mark R. Boothby, Hu Zeng
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-18 (2024)
Abstract Germinal center (GC) formation, which is an integrant part of humoral immunity, involves energy-consuming metabolic reprogramming. Rag-GTPases are known to signal amino acid availability to cellular pathways that regulate nutrient distributi
Externí odkaz:
https://doaj.org/article/b9a0aef0153c41448af1034c22be9cab
Publikováno v:
IET Image Processing, Vol 18, Iss 13, Pp 4328-4340 (2024)
Abstract Due to the powerful ability in capturing the global information, transformer has become an alternative architecture of CNNs for hyperspectral image classification. However, general transformer mainly considers the global spectral information
Externí odkaz:
https://doaj.org/article/9173a33cd3374f848168917aa44cffee