Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Fantacci, Claudio"'
Autor:
Bousmalis, Konstantinos, Vezzani, Giulia, Rao, Dushyant, Devin, Coline, Lee, Alex X., Bauza, Maria, Davchev, Todor, Zhou, Yuxiang, Gupta, Agrim, Raju, Akhil, Laurens, Antoine, Fantacci, Claudio, Dalibard, Valentin, Zambelli, Martina, Martins, Murilo, Pevceviciute, Rugile, Blokzijl, Michiel, Denil, Misha, Batchelor, Nathan, Lampe, Thomas, Parisotto, Emilio, Żołna, Konrad, Reed, Scott, Colmenarejo, Sergio Gómez, Scholz, Jon, Abdolmaleki, Abbas, Groth, Oliver, Regli, Jean-Baptiste, Sushkov, Oleg, Rothörl, Tom, Chen, José Enrique, Aytar, Yusuf, Barker, Dave, Ortiz, Joy, Riedmiller, Martin, Springenberg, Jost Tobias, Hadsell, Raia, Nori, Francesco, Heess, Nicolas
The ability to leverage heterogeneous robotic experience from different robots and tasks to quickly master novel skills and embodiments has the potential to transform robot learning. Inspired by recent advances in foundation models for vision and lan
Externí odkaz:
http://arxiv.org/abs/2306.11706
Autor:
Scarpellini, Gianluca, Konyushkova, Ksenia, Fantacci, Claudio, Paine, Tom Le, Chen, Yutian, Denil, Misha
This paper describes $\pi2\text{vec}$, a method for representing behaviors of black box policies as feature vectors. The policy representations capture how the statistics of foundation model features change in response to the policy behavior in a tas
Externí odkaz:
http://arxiv.org/abs/2306.09800
Autor:
Sharma, Mohit, Fantacci, Claudio, Zhou, Yuxiang, Koppula, Skanda, Heess, Nicolas, Scholz, Jon, Aytar, Yusuf
Recent works have shown that large models pretrained on common visual learning tasks can provide useful representations for a wide range of specialized perception problems, as well as a variety of robotic manipulation tasks. While prior work on robot
Externí odkaz:
http://arxiv.org/abs/2304.06600
Autor:
Vezzani, Giulia, Tirumala, Dhruva, Wulfmeier, Markus, Rao, Dushyant, Abdolmaleki, Abbas, Moran, Ben, Haarnoja, Tuomas, Humplik, Jan, Hafner, Roland, Neunert, Michael, Fantacci, Claudio, Hertweck, Tim, Lampe, Thomas, Sadeghi, Fereshteh, Heess, Nicolas, Riedmiller, Martin
The ability to effectively reuse prior knowledge is a key requirement when building general and flexible Reinforcement Learning (RL) agents. Skill reuse is one of the most common approaches, but current methods have considerable limitations.For examp
Externí odkaz:
http://arxiv.org/abs/2211.13743
Autor:
Lee, Alex X., Devin, Coline, Zhou, Yuxiang, Lampe, Thomas, Bousmalis, Konstantinos, Springenberg, Jost Tobias, Byravan, Arunkumar, Abdolmaleki, Abbas, Gileadi, Nimrod, Khosid, David, Fantacci, Claudio, Chen, Jose Enrique, Raju, Akhil, Jeong, Rae, Neunert, Michael, Laurens, Antoine, Saliceti, Stefano, Casarini, Federico, Riedmiller, Martin, Hadsell, Raia, Nori, Francesco
We study the problem of robotic stacking with objects of complex geometry. We propose a challenging and diverse set of such objects that was carefully designed to require strategies beyond a simple "pick-and-place" solution. Our method is a reinforce
Externí odkaz:
http://arxiv.org/abs/2110.06192
Autor:
Murali, Prajval Kumar, Sorrentino, Ines, Rendiniello, Angelo, Fantacci, Claudio, Villagrossi, Enrico, Polo, Andrea, Ardesi, Alessandro, Maggiali, Marco, Natale, Lorenzo, Pucci, Daniele, Traversaro, Silvio
Publikováno v:
IEEE International Conference on Robotics and Automation, 2021
Hand-eye calibration of laser profile sensors is the process of extracting the homogeneous transformation between the laser profile sensor frame and the end-effector frame of a robot in order to express the data extracted by the sensor in the robot's
Externí odkaz:
http://arxiv.org/abs/2103.11981
Publikováno v:
IEEE International Conference on Robotics and Automation (ICRA), 2018
To precisely reach for an object with a humanoid robot, it is of central importance to have good knowledge of both end-effector, object pose and shape. In this work we propose a framework for markerless visual servoing on unknown objects, which is di
Externí odkaz:
http://arxiv.org/abs/1710.04465
Publikováno v:
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017
This paper addresses recursive markerless estimation of a robot's end-effector using visual observations from its cameras. The problem is formulated into the Bayesian framework and addressed using Sequential Monte Carlo (SMC) filtering. We use a 3D r
Externí odkaz:
http://arxiv.org/abs/1703.04771
This overview paper describes the particle methods developed for the implementation of the a class of Bayes filters formulated using the random finite set formalism. It is primarily intended for the readership already familiar with the particle metho
Externí odkaz:
http://arxiv.org/abs/1602.03945
Autor:
Fantacci, Claudio
The aim of the present dissertation is to address distributed tracking over a network of heterogeneous and geographically dispersed nodes (or agents) with sensing, communication and processing capabilities. Tracking is carried out in the Bayesian fra
Externí odkaz:
http://arxiv.org/abs/1508.04158