Zobrazeno 1 - 10
of 86 448
pro vyhledávání: '"A, Gianni'"'
We present BricksRL, a platform designed to democratize access to robotics for reinforcement learning research and education. BricksRL facilitates the creation, design, and training of custom LEGO robots in the real world by interfacing them with the
Externí odkaz:
http://arxiv.org/abs/2406.17490
Autor:
Rieffel, Eleanor G., Asanjan, Ata Akbari, Alam, M. Sohaib, Anand, Namit, Neira, David E. Bernal, Block, Sophie, Brady, Lucas T., Cotton, Steve, Izquierdo, Zoe Gonzalez, Grabbe, Shon, Gustafson, Erik, Hadfield, Stuart, Lott, P. Aaron, Maciejewski, Filip B., Mandrà, Salvatore, Marshall, Jeffrey, Mossi, Gianni, Bauza, Humberto Munoz, Saied, Jason, Suri, Nishchay, Venturelli, Davide, Wang, Zhihui, Biswas, Rupak
Publikováno v:
Future Generation Computer Systems (2024)
Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum computing hardware has advanced rapidly, from tiny laboratory experiment
Externí odkaz:
http://arxiv.org/abs/2406.15601
Autor:
Müller, Raphael, Allevato, Gianni, Rutsch, Matthias, Haugwitz, Christoph, Kupnik, Mario, Pesavento, Marius
Arrays of ultrasonic sensors are capable of 3D imaging in air and an affordable supplement to other sensing modalities, such as radar, lidar, and camera, i.e. in heterogeneous sensing systems. However, manufacturing tolerances of air-coupled ultrason
Externí odkaz:
http://arxiv.org/abs/2406.14355
Vision-Language Models seamlessly discriminate among arbitrary semantic categories, yet they still suffer from poor generalization when presented with challenging examples. For this reason, Episodic Test-Time Adaptation (TTA) strategies have recently
Externí odkaz:
http://arxiv.org/abs/2405.18330
Autor:
Bou, Albert, Thomas, Morgan, Dittert, Sebastian, Ramírez, Carles Navarro, Majewski, Maciej, Wang, Ye, Patel, Shivam, Tresadern, Gary, Ahmad, Mazen, Moens, Vincent, Sherman, Woody, Sciabola, Simone, De Fabritiis, Gianni
In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecules with desired properties. However, striking a balance between capabilities, flexibility, reliability,
Externí odkaz:
http://arxiv.org/abs/2405.04657
Autor:
Fuchs, Jared, Helmerich, Christopher, Bobrick, Alexey, Sellers, Luke, Melcher, Brandon, Martire, Gianni
Publikováno v:
Jared Fuchs et al 2024 Class. Quantum Grav. 41 095013
Warp drives are exotic solutions of general relativity that offer novel means of transportation. In this study, we present a solution for a constant-velocity subluminal warp drive that satisfies all of the energy conditions. The solution involves com
Externí odkaz:
http://arxiv.org/abs/2405.02709
We propose a hybrid formulation of the linear inverted pendulum model for bipedal locomotion, where the foot switches are triggered based on the center of mass position, removing the need for pre-defined footstep timings. Using a concept similar to r
Externí odkaz:
http://arxiv.org/abs/2405.02184
In next-generation vehicular environments, precise localization is crucial for facilitating advanced applications such as autonomous driving. As automation levels escalate, the demand rises for enhanced accuracy, reliability, energy efficiency, updat
Externí odkaz:
http://arxiv.org/abs/2404.14206
Autor:
Helmerich, Christopher, Fuchs, Jared, Bobrick, Alexey, Melcher, Brandon, Sellers, Luke, Martire, Gianni
The last few decades of warp drive research have focused on analytic methods to explore warp solutions to Einstein's field equations. These analytic solutions tend to favor simple metric forms which are easier to analyze but limit the space of explor
Externí odkaz:
http://arxiv.org/abs/2404.10855
Autor:
DeAndres-Tame, Ivan, Tolosana, Ruben, Melzi, Pietro, Vera-Rodriguez, Ruben, Kim, Minchul, Rathgeb, Christian, Liu, Xiaoming, Morales, Aythami, Fierrez, Julian, Ortega-Garcia, Javier, Zhong, Zhizhou, Huang, Yuge, Mi, Yuxi, Ding, Shouhong, Zhou, Shuigeng, He, Shuai, Fu, Lingzhi, Cong, Heng, Zhang, Rongyu, Xiao, Zhihong, Smirnov, Evgeny, Pimenov, Anton, Grigorev, Aleksei, Timoshenko, Denis, Asfaw, Kaleb Mesfin, Low, Cheng Yaw, Liu, Hao, Wang, Chuyi, Zuo, Qing, He, Zhixiang, Shahreza, Hatef Otroshi, George, Anjith, Unnervik, Alexander, Rahimi, Parsa, Marcel, Sébastien, Neto, Pedro C., Huber, Marco, Kolf, Jan Niklas, Damer, Naser, Boutros, Fadi, Cardoso, Jaime S., Sequeira, Ana F., Atzori, Andrea, Fenu, Gianni, Marras, Mirko, Štruc, Vitomir, Yu, Jiang, Li, Zhangjie, Li, Jichun, Zhao, Weisong, Lei, Zhen, Zhu, Xiangyu, Zhang, Xiao-Yu, Biesseck, Bernardo, Vidal, Pedro, Coelho, Luiz, Granada, Roger, Menotti, David
Publikováno v:
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRw 2024)
Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class variability, time and errors produced in manual labeling, and in some c
Externí odkaz:
http://arxiv.org/abs/2404.10378