Zobrazeno 1 - 10
of 552
pro vyhledávání: '"Ding, Wenbo"'
The robotic dexterous hand is responsible for both grasping and dexterous manipulation. The number of motors directly influences both the dexterity and the cost of such systems. In this paper, we present MuxHand, a robotic hand that employs a time-di
Externí odkaz:
http://arxiv.org/abs/2409.12455
Controlling hands in the high-dimensional action space has been a longstanding challenge, yet humans naturally perform dexterous tasks with ease. In this paper, we draw inspiration from the human embodied cognition and reconsider dexterous hands as l
Externí odkaz:
http://arxiv.org/abs/2409.10983
Autor:
Lai, Jiahao, Li, Jiaqi, Xu, Jian, Wu, Yanru, Tang, Boshi, Chen, Siqi, Huang, Yongfeng, Ding, Wenbo, Li, Yang
Federated Learning (FL) offers a decentralized approach to model training, where data remains local and only model parameters are shared between the clients and the central server. Traditional methods, such as Federated Averaging (FedAvg), linearly a
Externí odkaz:
http://arxiv.org/abs/2409.05701
Transparent objects are common in daily life, while their optical properties pose challenges for RGB-D cameras to capture accurate depth information. This issue is further amplified when these objects are hand-held, as hand occlusions further complic
Externí odkaz:
http://arxiv.org/abs/2408.14997
Autor:
Zhang, Ruize, Xu, Zelai, Ma, Chengdong, Yu, Chao, Tu, Wei-Wei, Huang, Shiyu, Ye, Deheng, Ding, Wenbo, Yang, Yaodong, Wang, Yu
Self-play, characterized by agents' interactions with copies or past versions of itself, has recently gained prominence in reinforcement learning. This paper first clarifies the preliminaries of self-play, including the multi-agent reinforcement lear
Externí odkaz:
http://arxiv.org/abs/2408.01072
Large pre-trained models, such as large language models (LLMs), present significant resource challenges for fine-tuning due to their extensive parameter sizes, especially for applications in mobile systems. To address this, Low-Rank Adaptation (LoRA)
Externí odkaz:
http://arxiv.org/abs/2407.12074
Autor:
Li, Shoujie, Wang, Zihan, Wu, Changsheng, Li, Xiang, Luo, Shan, Fang, Bin, Sun, Fuchun, Zhang, Xiao-Ping, Ding, Wenbo
Tactile sensors, which provide information about the physical properties of objects, are an essential component of robotic systems. The visuotactile sensing technology with the merits of high resolution and low cost has facilitated the development of
Externí odkaz:
http://arxiv.org/abs/2406.12226
The advent of simulation engines has revolutionized learning and operational efficiency for robots, offering cost-effective and swift pipelines. However, the lack of a universal simulation platform tailored for chemical scenarios impedes progress in
Externí odkaz:
http://arxiv.org/abs/2406.08160
Although large-scale pre-trained models hold great potential for adapting to downstream tasks through fine-tuning, the performance of such fine-tuned models is often limited by the difficulty of collecting sufficient high-quality, task-specific data.
Externí odkaz:
http://arxiv.org/abs/2404.15384
When addressing the challenge of complex multi-objective optimization problems, particularly those with non-convex and non-uniform Pareto fronts, Decomposition-based Multi-Objective Evolutionary Algorithms (MOEADs) often converge to local optima, the
Externí odkaz:
http://arxiv.org/abs/2404.08501