Zobrazeno 1 - 10
of 2 214
pro vyhledávání: '"Sferrazza A"'
Autor:
As, Yarden, Sukhija, Bhavya, Treven, Lenart, Sferrazza, Carmelo, Coros, Stelian, Krause, Andreas
Reinforcement learning (RL) is ubiquitous in the development of modern AI systems. However, state-of-the-art RL agents require extensive, and potentially unsafe, interactions with their environments to learn effectively. These limitations confine RL
Externí odkaz:
http://arxiv.org/abs/2410.09486
Autor:
Singh, Himanshu Gaurav, Loquercio, Antonio, Sferrazza, Carmelo, Wu, Jane, Qi, Haozhi, Abbeel, Pieter, Malik, Jitendra
We present an approach to learn general robot manipulation priors from 3D hand-object interaction trajectories. We build a framework to use in-the-wild videos to generate sensorimotor robot trajectories. We do so by lifting both the human hand and th
Externí odkaz:
http://arxiv.org/abs/2409.08273
In recent years, the transformer architecture has become the de facto standard for machine learning algorithms applied to natural language processing and computer vision. Despite notable evidence of successful deployment of this architecture in the c
Externí odkaz:
http://arxiv.org/abs/2408.06316
Autor:
Acharya, B., Aliotta, M., Balantekin, A. B., Bemmerer, D., Bertulani, C. A., Best, A., Brune, C. R., Buompane, R., Cavanna, F., Chen, J. W., Colgan, J., Czarnecki, A., Davids, B., deBoer, R. J., Delahaye, F., Depalo, R., García, A., Johnson, M. Gatu, Gazit, D., Gialanella, L., Greife, U., Guffanti, D., Guglielmetti, A., Hambleton, K., Haxton, W. C., Herrera, Y., Huang, M., Iliadis, C., Kravvaris, K., La Cognata, M., Langanke, K., Marcucci, L. E., Nagayama, T., Nollett, K. M., Odell, D., Gann, G. D. Orebi, Piatti, D., Pinsonneault, M., Platter, L., Robertson, R. G. H., Rupak, G., Serenelli, A., Sferrazza, M., Szücs, T., Tang, X., Tumino, A., Villante, F. L., Walker-Loud, A., Zhang, X., Zuber, K.
In stars that lie on the main sequence in the Hertzsprung Russel diagram, like our sun, hydrogen is fused to helium in a number of nuclear reaction chains and series, such as the proton-proton chain and the carbon-nitrogen-oxygen cycles. Precisely de
Externí odkaz:
http://arxiv.org/abs/2405.06470
Humanoid robots hold great promise in assisting humans in diverse environments and tasks, due to their flexibility and adaptability leveraging human-like morphology. However, research in humanoid robots is often bottlenecked by the costly and fragile
Externí odkaz:
http://arxiv.org/abs/2403.10506
One of the main challenges in the area of Neuro-Symbolic AI is to perform logical reasoning in the presence of both neural and symbolic data. This requires combining heterogeneous data sources such as knowledge graphs, neural model predictions, struc
Externí odkaz:
http://arxiv.org/abs/2403.02933
Distributed tactile sensing for multi-force detection is crucial for various aerial robot interaction tasks. However, current contact sensing solutions on drones only exploit single end-effector sensors and cannot provide distributed multi-contact se
Externí odkaz:
http://arxiv.org/abs/2401.17149
Humans rely on the synergy of their senses for most essential tasks. For tasks requiring object manipulation, we seamlessly and effectively exploit the complementarity of our senses of vision and touch. This paper draws inspiration from such capabili
Externí odkaz:
http://arxiv.org/abs/2311.00924
Autor:
Adeniji, Ademi, Xie, Amber, Sferrazza, Carmelo, Seo, Younggyo, James, Stephen, Abbeel, Pieter
Using learned reward functions (LRFs) as a means to solve sparse-reward reinforcement learning (RL) tasks has yielded some steady progress in task-complexity through the years. In this work, we question whether today's LRFs are best-suited as a direc
Externí odkaz:
http://arxiv.org/abs/2308.12270
Autor:
Michele Amata, MD, Giulio Calabrese, MD, Daniela Scimeca, PhD, Barbara Scrivo, MD, Ambra Bonaccorso, MD, Sandro Sferrazza, MD, Elisabetta Conte, MD, Roberto Di Mitri, MD
Publikováno v:
VideoGIE, Vol 9, Iss 11, Pp 481-483 (2024)
Externí odkaz:
https://doaj.org/article/41524bee6ef6497cb0bf7ecf8e13c6e6
