Zobrazeno 1 - 10
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pro vyhledávání: '"WANG, Junbo"'
USDRL: Unified Skeleton-Based Dense Representation Learning with Multi-Grained Feature Decorrelation
Contrastive learning has achieved great success in skeleton-based representation learning recently. However, the prevailing methods are predominantly negative-based, necessitating additional momentum encoder and memory bank to get negative samples, w
Externí odkaz:
http://arxiv.org/abs/2412.09220
In most contact-rich manipulation tasks, humans apply time-varying forces to the target object, compensating for inaccuracies in the vision-guided hand trajectory. However, current robot learning algorithms primarily focus on trajectory-based policy,
Externí odkaz:
http://arxiv.org/abs/2410.07554
Autor:
Yu, Qiaojun, Huang, Siyuan, Yuan, Xibin, Jiang, Zhengkai, Hao, Ce, Li, Xin, Chang, Haonan, Wang, Junbo, Liu, Liu, Li, Hongsheng, Gao, Peng, Lu, Cewu
Previous studies on robotic manipulation are based on a limited understanding of the underlying 3D motion constraints and affordances. To address these challenges, we propose a comprehensive paradigm, termed UniAff, that integrates 3D object-centric
Externí odkaz:
http://arxiv.org/abs/2409.20551
Federated learning (FL), as an emerging collaborative learning paradigm, has garnered significant attention due to its capacity to preserve privacy within distributed learning systems. In these systems, clients collaboratively train a unified neural
Externí odkaz:
http://arxiv.org/abs/2405.17522
Articulated objects are commonly found in daily life. It is essential that robots can exhibit robust perception and manipulation skills for articulated objects in real-world robotic applications. However, existing methods for articulated objects insu
Externí odkaz:
http://arxiv.org/abs/2403.16023
Autor:
Yu, Qiaojun, Hao, Ce, Wang, Junbo, Liu, Wenhai, Liu, Liu, Mu, Yao, You, Yang, Yan, Hengxu, Lu, Cewu
Robotic manipulation in everyday scenarios, especially in unstructured environments, requires skills in pose-aware object manipulation (POM), which adapts robots' grasping and handling according to an object's 6D pose. Recognizing an object's positio
Externí odkaz:
http://arxiv.org/abs/2403.13365
Traffic forecasting, which benefits from mobile Internet development and position technologies, plays a critical role in Intelligent Transportation Systems. It helps to implement rich and varied transportation applications and bring convenient transp
Externí odkaz:
http://arxiv.org/abs/2310.16070
Articulated objects like cabinets and doors are widespread in daily life. However, directly manipulating 3D articulated objects is challenging because they have diverse geometrical shapes, semantic categories, and kinetic constraints. Prior works mos
Externí odkaz:
http://arxiv.org/abs/2309.16264
Autor:
Fang, Hao-Shu, Fang, Hongjie, Tang, Zhenyu, Liu, Jirong, Wang, Chenxi, Wang, Junbo, Zhu, Haoyi, Lu, Cewu
A key challenge in robotic manipulation in open domains is how to acquire diverse and generalizable skills for robots. Recent research in one-shot imitation learning has shown promise in transferring trained policies to new tasks based on demonstrati
Externí odkaz:
http://arxiv.org/abs/2307.00595
Publikováno v:
Cailiao Baohu, Vol 57, Iss 8, Pp 192-197 (2024)
Aiming at the problem of early fatigue cracks in plasma nitrided H13 steel precision hot forging dies， plasma nitriding process tests were conducted under a nitriding temperature of 520 ℃， a pressure of 250 Pa and different nitrogen to hydrogen
Externí odkaz:
https://doaj.org/article/c625941242534487bcb3abe1e05f963c
