Zobrazeno 1 - 10
of 1 087
pro vyhledávání: '"P, Régli"'
Autor:
Paul, Taylor, Regli, William
In this position paper we argue for standardizing how we share and process data in scientific workflows at the network-level to maximize step re-use and workflow portability across platforms and networks in pursuit of a foundational workflow stack. W
Externí odkaz:
http://arxiv.org/abs/2412.13339
This paper introduces a novel approach to ontology-based robot plan transfer using functorial data migrations from category theory. Functors provide structured maps between domain types and predicates which can be used to transfer plans from a source
Externí odkaz:
http://arxiv.org/abs/2406.15961
Autor:
Sarwin, Gary, Carretta, Alessandro, Staartjes, Victor, Zoli, Matteo, Mazzatenta, Diego, Regli, Luca, Serra, Carlo, Konukoglu, Ender
Localizing oneself during endoscopic procedures can be problematic due to the lack of distinguishable textures and landmarks, as well as difficulties due to the endoscopic device such as a limited field of view and challenging lighting conditions. Ex
Externí odkaz:
http://arxiv.org/abs/2405.09355
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
Classical planning representation languages based on first-order logic have preliminarily been used to model and solve robotic task planning problems. Wider adoption of these representation languages, however, is hindered by the limitations present w
Externí odkaz:
http://arxiv.org/abs/2305.17208
Autor:
Sarwin, Gary, Carretta, Alessandro, Staartjes, Victor, Zoli, Matteo, Mazzatenta, Diego, Regli, Luca, Serra, Carlo, Konukoglu, Ender
Advanced minimally invasive neurosurgery navigation relies mainly on Magnetic Resonance Imaging (MRI) guidance. MRI guidance, however, only provides pre-operative information in the majority of the cases. Once the surgery begins, the value of this gu
Externí odkaz:
http://arxiv.org/abs/2303.18019
Autor:
Davchev, Todor, Sushkov, Oleg, Regli, Jean-Baptiste, Schaal, Stefan, Aytar, Yusuf, Wulfmeier, Markus, Scholz, Jon
Publikováno v:
International Conference on Learning Representations (ICLR 2022)
Complex sequential tasks in continuous-control settings often require agents to successfully traverse a set of "narrow passages" in their state space. Solving such tasks with a sparse reward in a sample-efficient manner poses a challenge to modern re
Externí odkaz:
http://arxiv.org/abs/2112.00597
With Moore's law coming to a close it is useful to look at other forms of computer hardware. In this paper we survey what is known about several modes of computation: Neuromorphic, Custom Logic, Quantum, Optical, Spintronics, Reversible, Many-Valued
Externí odkaz:
http://arxiv.org/abs/2111.08916
Autor:
Aguinaldo, Angeline, Regli, William
Classical AI planners provide solutions to planning problems in the form of long and opaque text outputs. To aid in the understanding transferability of planning solutions, it is necessary to have a rich and comprehensible representation for both hum
Externí odkaz:
http://arxiv.org/abs/2107.05850
Autor:
Vecerik, Mel, Regli, Jean-Baptiste, Sushkov, Oleg, Barker, David, Pevceviciute, Rugile, Rothörl, Thomas, Schuster, Christopher, Hadsell, Raia, Agapito, Lourdes, Scholz, Jonathan
A robot's ability to act is fundamentally constrained by what it can perceive. Many existing approaches to visual representation learning utilize general-purpose training criteria, e.g. image reconstruction, smoothness in latent space, or usefulness
Externí odkaz:
http://arxiv.org/abs/2009.14711