Zobrazeno 101 - 110
of 1 946
pro vyhledávání: '"Rodriguez, Alberto"'
Autor:
Zeng, Andy, Song, Shuran, Welker, Stefan, Lee, Johnny, Rodriguez, Alberto, Funkhouser, Thomas
Skilled robotic manipulation benefits from complex synergies between non-prehensile (e.g. pushing) and prehensile (e.g. grasping) actions: pushing can help rearrange cluttered objects to make space for arms and fingers; likewise, grasping can help di
Externí odkaz:
http://arxiv.org/abs/1803.09956
Autor:
Yu, Kuan-Ting, Rodriguez, Alberto
We develop a real-time state estimation system to recover the pose and contact formation of an object relative to its environment. In this paper, we focus on the application of inserting an object picked by a suction cup into a tight space, an enabli
Externí odkaz:
http://arxiv.org/abs/1803.08014
Publikováno v:
IROS 2018
This paper presents a novel regrasp control policy that makes use of tactile sensing to plan local grasp adjustments. Our approach determines regrasp actions by virtually searching for local transformations of tactile measurements that improve the qu
Externí odkaz:
http://arxiv.org/abs/1803.01940
This work describes the development of a high-resolution tactile-sensing finger for robot grasping. This finger, inspired by previous GelSight sensing techniques, features an integration that is slimmer, more robust, and with more homogeneous output
Externí odkaz:
http://arxiv.org/abs/1803.00628
Autor:
Ma, Daolin, Rodriguez, Alberto
Friction plays a key role in manipulating objects. Most of what we do with our hands, and most of what robots do with their grippers, is based on the ability to control frictional forces. This paper aims to better understand the variability and predi
Externí odkaz:
http://arxiv.org/abs/1802.10089
This paper presents an approach to in-hand manipulation planning that exploits the mechanics of alternating sticking contact. Particularly, we consider the problem of manipulating a grasped object using external pushes for which the pusher sticks to
Externí odkaz:
http://arxiv.org/abs/1710.11097
Publikováno v:
Phys. Rev. Lett. 120, 210401 (2018)
The dynamics of bosons in generic multimode systems, such as Bose-Hubbard models, is not only determined by interactions among the particles, but also by their mutual indistinguishability manifested in many-particle interference. We introduce a measu
Externí odkaz:
http://arxiv.org/abs/1710.08876
In this paper we demonstrate the limitations of common rigid-body contact models used in the robotics community by comparing them to a collection of data-driven and data-reinforced models that exploit underlying structure inspired by the rigid contac
Externí odkaz:
http://arxiv.org/abs/1710.05947
This paper presents a reactive controller for planar manipulation tasks that leverages machine learning to achieve real-time performance. The approach is based on a Model Predictive Control (MPC) formulation, where the goal is to find an optimal sequ
Externí odkaz:
http://arxiv.org/abs/1710.05724
The ability to reason about and predict the outcome of contacts is paramount to the successful execution of many robot tasks. Analytical rigid-body contact models are used extensively in planning and control due to their computational efficiency and
Externí odkaz:
http://arxiv.org/abs/1710.04979