Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Lian, Wenzhao"'
Autor:
Liu, Litao, Wang, Wentao, Han, Yifan, Xie, Zhuoli, Yi, Pengfei, Li, Junyan, Qin, Yi, Lian, Wenzhao
Multi-task imitation learning (MTIL) has shown significant potential in robotic manipulation by enabling agents to perform various tasks using a unified policy. This simplifies the policy deployment and enhances the agent's adaptability across differ
Externí odkaz:
http://arxiv.org/abs/2409.19528
Manipulating cables is challenging for robots because of the infinite degrees of freedom of the cables and frequent occlusion by the gripper and the environment. These challenges are further complicated by the dexterous nature of the operations requi
Externí odkaz:
http://arxiv.org/abs/2303.11765
Learning generalizable insertion skills in a data-efficient manner has long been a challenge in the robot learning community. While the current state-of-the-art methods with reinforcement learning (RL) show promising performance in acquiring manipula
Externí odkaz:
http://arxiv.org/abs/2212.00955
Autor:
Lian, Wenzhao
The analysis of time series and sequences has been challenging in both statistics and machine learning community, because of their properties including high dimensionality, pattern dynamics, and irregular observations. In this thesis, novel methods a
Externí odkaz:
http://hdl.handle.net/10161/11362
Allowing Safe Contact in Robotic Goal-Reaching: Planning and Tracking in Operational and Null Spaces
In recent years, impressive results have been achieved in robotic manipulation. While many efforts focus on generating collision-free reference signals, few allow safe contact between the robot bodies and the environment. However, in human's daily ma
Externí odkaz:
http://arxiv.org/abs/2211.08199
Autor:
Wu, Zheng, Xie, Yichen, Lian, Wenzhao, Wang, Changhao, Guo, Yanjiang, Chen, Jianyu, Schaal, Stefan, Tomizuka, Masayoshi
Humans are capable of abstracting various tasks as different combinations of multiple attributes. This perspective of compositionality is vital for human rapid learning and adaption since previous experiences from related tasks can be combined to gen
Externí odkaz:
http://arxiv.org/abs/2210.00350
Manipulation tasks often require a robot to adjust its sensorimotor skills based on the state it finds itself in. Taking peg-in-hole as an example: once the peg is aligned with the hole, the robot should push the peg downwards. While high level execu
Externí odkaz:
http://arxiv.org/abs/2203.02468
Publikováno v:
Robotics: Science and Systems (RSS) 2022
Promising results have been achieved recently in category-level manipulation that generalizes across object instances. Nevertheless, it often requires expensive real-world data collection and manual specification of semantic keypoints for each object
Externí odkaz:
http://arxiv.org/abs/2201.12716
Task-relevant grasping is critical for industrial assembly, where downstream manipulation tasks constrain the set of valid grasps. Learning how to perform this task, however, is challenging, since task-relevant grasp labels are hard to define and ann
Externí odkaz:
http://arxiv.org/abs/2109.09163
Autor:
Luo, Jianlan, Sushkov, Oleg, Pevceviciute, Rugile, Lian, Wenzhao, Su, Chang, Vecerik, Mel, Ye, Ning, Schaal, Stefan, Scholz, Jon
Over the past several years there has been a considerable research investment into learning-based approaches to industrial assembly, but despite significant progress these techniques have yet to be adopted by industry. We argue that it is the prohibi
Externí odkaz:
http://arxiv.org/abs/2103.11512