Zobrazeno 1 - 10
of 1 380
pro vyhledávání: '"RUS, Daniela"'
The deployment of autonomous vehicles controlled by machine learning techniques requires extensive testing in diverse real-world environments, robust handling of edge cases and out-of-distribution scenarios, and comprehensive safety validation to ens
Externí odkaz:
http://arxiv.org/abs/2411.16554
In this work, we consider the problem of learning end to end perception to control for ground vehicles solely from aerial imagery. Photogrammetric simulators allow the synthesis of novel views through the transformation of pre-generated assets into n
Externí odkaz:
http://arxiv.org/abs/2410.14177
End-to-end learning directly maps sensory inputs to actions, creating highly integrated and efficient policies for complex robotics tasks. However, such models are tricky to efficiently train and often struggle to generalize beyond their training sce
Externí odkaz:
http://arxiv.org/abs/2410.13002
Autor:
Werner, Peter, Cohn, Thomas, Jiang, Rebecca H., Seyde, Tim, Simchowitz, Max, Tedrake, Russ, Rus, Daniela
We propose two novel algorithms for constructing convex collision-free polytopes in robot configuration space. Finding these polytopes enables the application of stronger motion-planning frameworks such as trajectory optimization with Graphs of Conve
Externí odkaz:
http://arxiv.org/abs/2410.12649
Autor:
Rusch, T. Konstantin, Rus, Daniela
We propose Linear Oscillatory State-Space models (LinOSS) for efficiently learning on long sequences. Inspired by cortical dynamics of biological neural networks, we base our proposed LinOSS model on a system of forced harmonic oscillators. A stable
Externí odkaz:
http://arxiv.org/abs/2410.03943
Autor:
Chen, Peter Yichen, Liu, Chao, Ma, Pingchuan, Eastman, John, Rus, Daniela, Randle, Dylan, Ivanov, Yuri, Matusik, Wojciech
Differentiable simulation has become a powerful tool for system identification. While prior work has focused on identifying robot properties using robot-specific data or object properties using object-specific data, our approach calibrates object pro
Externí odkaz:
http://arxiv.org/abs/2410.03920
Sampling-based motion planning methods, while effective in high-dimensional spaces, often suffer from inefficiencies due to irregular sampling distributions, leading to suboptimal exploration of the configuration space. In this paper, we propose an a
Externí odkaz:
http://arxiv.org/abs/2410.03909
Autor:
Makiyeh, Fouad, Bastourous, Mark, Bairouk, Anass, Xiao, Wei, Maras, Mirjana, Wangb, Tsun-Hsuan, Blanchon, Marc, Hasani, Ramin, Chareyre, Patrick, Rus, Daniela
Autonomous vehicle navigation is a key challenge in artificial intelligence, requiring robust and accurate decision-making processes. This research introduces a new end-to-end method that exploits multimodal information from a single monocular camera
Externí odkaz:
http://arxiv.org/abs/2409.12716
Autor:
Nguyen, Huy-Dung, Bairouk, Anass, Maras, Mirjana, Xiao, Wei, Wang, Tsun-Hsuan, Chareyre, Patrick, Hasani, Ramin, Blanchon, Marc, Rus, Daniela
Autonomous driving holds great potential to transform road safety and traffic efficiency by minimizing human error and reducing congestion. A key challenge in realizing this potential is the accurate estimation of steering angles, which is essential
Externí odkaz:
http://arxiv.org/abs/2409.10095
Soft robotics focuses on designing robots with highly deformable materials, allowing them to adapt and operate safely and reliably in unstructured and variable environments. While soft robots offer increased compliance over rigid body robots, their p
Externí odkaz:
http://arxiv.org/abs/2408.09275