Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Petrik, Vladimir"'
Autor:
Cífka, Martin, Ponimatkin, Georgy, Labbé, Yann, Russell, Bryan, Aubry, Mathieu, Petrik, Vladimir, Sivic, Josef
We introduce FocalPose++, a neural render-and-compare method for jointly estimating the camera-object 6D pose and camera focal length given a single RGB input image depicting a known object. The contributions of this work are threefold. First, we der
Externí odkaz:
http://arxiv.org/abs/2312.02985
Autor:
Fourmy, Mederic, Priban, Vojtech, Behrens, Jan Kristof, Mansard, Nicolas, Sivic, Josef, Petrik, Vladimir
The objective of this work is to enable manipulation tasks with respect to the 6D pose of a dynamically moving object using a camera mounted on a robot. Examples include maintaining a constant relative 6D pose of the robot arm with respect to the obj
Externí odkaz:
http://arxiv.org/abs/2311.05344
Autor:
Montaut, Louis, Lidec, Quentin Le, Bambade, Antoine, Petrik, Vladimir, Sivic, Josef, Carpentier, Justin
Collision detection appears as a canonical operation in a large range of robotics applications from robot control to simulation, including motion planning and estimation. While the seminal works on the topic date back to the 80s, it is only recently
Externí odkaz:
http://arxiv.org/abs/2209.09012
We aim to teach robots to perform simple object manipulation tasks by watching a single video demonstration. Towards this goal, we propose an optimization approach that outputs a coarse and temporally evolving 3D scene to mimic the action demonstrate
Externí odkaz:
http://arxiv.org/abs/2208.01960
Publikováno v:
Robotics: Science and Systems 2022
Collision detection between two convex shapes is an essential feature of any physics engine or robot motion planner. It has often been tackled as a computational geometry problem, with the Gilbert, Johnson and Keerthi (GJK) algorithm being the most c
Externí odkaz:
http://arxiv.org/abs/2205.09663
A seamless integration of robots into human environments requires robots to learn how to use existing human tools. Current approaches for learning tool manipulation skills mostly rely on expert demonstrations provided in the target robot environment,
Externí odkaz:
http://arxiv.org/abs/2111.03088
Humans are adept at learning new tasks by watching a few instructional videos. On the other hand, robots that learn new actions either require a lot of effort through trial and error, or use expert demonstrations that are challenging to obtain. In th
Externí odkaz:
http://arxiv.org/abs/2011.06813
Autor:
Petrík, Vladimír, Kyrki, Ville
Accurate manipulation of a deformable body such as a piece of fabric is difficult because of its many degrees of freedom and unobservable properties affecting its dynamics. To alleviate these challenges, we propose the application of feedback-based c
Externí odkaz:
http://arxiv.org/abs/1904.01298
Planning accurate manipulation for deformable objects requires prediction of their state. The prediction is often complicated by a loss of stability that may result in collapse of the deformable object. In this work, stability of a fabric strip foldi
Externí odkaz:
http://arxiv.org/abs/1902.11021