Zobrazeno 1 - 10
of 141
pro vyhledávání: '"Weng, Thomas"'
Autor:
Biza, Ondrej, Weng, Thomas, Sun, Lingfeng, Schmeckpeper, Karl, Kelestemur, Tarik, Ma, Yecheng Jason, Platt, Robert, van de Meent, Jan-Willem, Wong, Lawson L. S.
Reinforcement Learning (RL) has the potential to enable robots to learn from their own actions in the real world. Unfortunately, RL can be prohibitively expensive, in terms of on-robot runtime, due to inefficient exploration when learning from a spar
Externí odkaz:
http://arxiv.org/abs/2410.19989
We formulate grasp learning as a neural field and present Neural Grasp Distance Fields (NGDF). Here, the input is a 6D pose of a robot end effector and output is a distance to a continuous manifold of valid grasps for an object. In contrast to curren
Externí odkaz:
http://arxiv.org/abs/2211.02647
Robotic manipulation of cloth has applications ranging from fabrics manufacturing to handling blankets and laundry. Cloth manipulation is challenging for robots largely due to their high degrees of freedom, complex dynamics, and severe self-occlusion
Externí odkaz:
http://arxiv.org/abs/2207.11196
We address the problem of goal-directed cloth manipulation, a challenging task due to the deformability of cloth. Our insight is that optical flow, a technique normally used for motion estimation in video, can also provide an effective representation
Externí odkaz:
http://arxiv.org/abs/2111.05623
Publikováno v:
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Cloth detection and manipulation is a common task in domestic and industrial settings, yet such tasks remain a challenge for robots due to cloth deformability. Furthermore, in many cloth-related tasks like laundry folding and bed making, it is crucia
Externí odkaz:
http://arxiv.org/abs/2008.05626
Publikováno v:
IEEE ROBOTICS AND AUTOMATION LETTERS, VOL. 5, NO. 3, JULY 2020. 3791-3798
State-of-the-art object grasping methods rely on depth sensing to plan robust grasps, but commercially available depth sensors fail to detect transparent and specular objects. To improve grasping performance on such objects, we introduce a method for
Externí odkaz:
http://arxiv.org/abs/2006.00028
Publikováno v:
ACM/IEEE International Conference on Human-Robot Interaction; Mar2016, p51-58, 8p
Publikováno v:
2016 IEEE International Conference on Robotics & Automation (ICRA); 2016, p3352-3359, 8p
Akademický článek
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Publikováno v:
Proceedings of the 2nd ACM Workshop: Embedded Sensing Systems for Energy-efficiency in Building; 11/ 2/2010, p1-6, 6p