Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Geng, Yiran"'
Autor:
Lou, Haozhe, Liu, Yurong, Pan, Yike, Geng, Yiran, Chen, Jianteng, Ma, Wenlong, Li, Chenglong, Wang, Lin, Feng, Hengzhen, Shi, Lu, Luo, Liyi, Shi, Yongliang
Real2Sim2Real plays a critical role in robotic arm control and reinforcement learning, yet bridging this gap remains a significant challenge due to the complex physical properties of robots and the objects they manipulate. Existing methods lack a com
Externí odkaz:
http://arxiv.org/abs/2408.14873
Learning a universal manipulation policy encompassing doors with diverse categories, geometries and mechanisms, is crucial for future embodied agents to effectively work in complex and broad real-world scenarios. Due to the limited datasets and unrea
Externí odkaz:
http://arxiv.org/abs/2403.02604
Autor:
Li, Xiaoqi, Zhang, Mingxu, Geng, Yiran, Geng, Haoran, Long, Yuxing, Shen, Yan, Zhang, Renrui, Liu, Jiaming, Dong, Hao
Robot manipulation relies on accurately predicting contact points and end-effector directions to ensure successful operation. However, learning-based robot manipulation, trained on a limited category within a simulator, often struggles to achieve gen
Externí odkaz:
http://arxiv.org/abs/2312.16217
Autor:
Li, Yuyang, Liu, Bo, Geng, Yiran, Li, Puhao, Yang, Yaodong, Zhu, Yixin, Liu, Tengyu, Huang, Siyuan
The intricate kinematics of the human hand enable simultaneous grasping and manipulation of multiple objects, essential for tasks such as object transfer and in-hand manipulation. Despite its significance, the domain of robotic multi-object grasping
Externí odkaz:
http://arxiv.org/abs/2310.15599
Autor:
Ji, Jiaming, Zhang, Borong, Zhou, Jiayi, Pan, Xuehai, Huang, Weidong, Sun, Ruiyang, Geng, Yiran, Zhong, Yifan, Dai, Juntao, Yang, Yaodong
Artificial intelligence (AI) systems possess significant potential to drive societal progress. However, their deployment often faces obstacles due to substantial safety concerns. Safe reinforcement learning (SafeRL) emerges as a solution to optimize
Externí odkaz:
http://arxiv.org/abs/2310.12567
Robotic manipulation requires accurate perception of the environment, which poses a significant challenge due to its inherent complexity and constantly changing nature. In this context, RGB image and point-cloud observations are two commonly used mod
Externí odkaz:
http://arxiv.org/abs/2310.03478
Autor:
Ji, Jiaming, Zhou, Jiayi, Zhang, Borong, Dai, Juntao, Pan, Xuehai, Sun, Ruiyang, Huang, Weidong, Geng, Yiran, Liu, Mickel, Yang, Yaodong
AI systems empowered by reinforcement learning (RL) algorithms harbor the immense potential to catalyze societal advancement, yet their deployment is often impeded by significant safety concerns. Particularly in safety-critical applications, research
Externí odkaz:
http://arxiv.org/abs/2305.09304
Learning a generalizable object manipulation policy is vital for an embodied agent to work in complex real-world scenes. Parts, as the shared components in different object categories, have the potential to increase the generalization ability of the
Externí odkaz:
http://arxiv.org/abs/2303.16958
In this work, we tackle 6-DoF grasp detection for transparent and specular objects, which is an important yet challenging problem in vision-based robotic systems, due to the failure of depth cameras in sensing their geometry. We, for the first time,
Externí odkaz:
http://arxiv.org/abs/2210.06575
Generating dexterous grasping has been a long-standing and challenging robotic task. Despite recent progress, existing methods primarily suffer from two issues. First, most prior arts focus on a specific type of robot hand, lacking the generalizable
Externí odkaz:
http://arxiv.org/abs/2210.00722