Zobrazeno 1 - 10
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pro vyhledávání: '"Tordesillas, A."'
Autor:
Talbot, William, Nubert, Julian, Tuna, Turcan, Cadena, Cesar, Dümbgen, Frederike, Tordesillas, Jesus, Barfoot, Timothy D., Hutter, Marco
Accurate, efficient, and robust state estimation is more important than ever in robotics as the variety of platforms and complexity of tasks continue to grow. Historically, discrete-time filters and smoothers have been the dominant approach, in which
Externí odkaz:
http://arxiv.org/abs/2411.03951
Autor:
Bagajo, Joshua, Schwarke, Clemens, Klemm, Victor, Georgiev, Ignat, Sleiman, Jean-Pierre, Tordesillas, Jesus, Garg, Animesh, Hutter, Marco
Differentiable simulators provide analytic gradients, enabling more sample-efficient learning algorithms and paving the way for data intensive learning tasks such as learning from images. In this work, we demonstrate that locomotion policies trained
Externí odkaz:
http://arxiv.org/abs/2411.02189
Autor:
Kondo, Kota, Tewari, Claudius T., Tagliabue, Andrea, Tordesillas, Jesus, Lusk, Parker C., How, Jonathan P.
In decentralized multiagent trajectory planners, agents need to communicate and exchange their positions to generate collision-free trajectories. However, due to localization errors/uncertainties, trajectory deconfliction can fail even if trajectorie
Externí odkaz:
http://arxiv.org/abs/2406.10060
The emergence of differentiable simulators enabling analytic gradient computation has motivated a new wave of learning algorithms that hold the potential to significantly increase sample efficiency over traditional Reinforcement Learning (RL) methods
Externí odkaz:
http://arxiv.org/abs/2404.02887
Publikováno v:
History of Geo- and Space Sciences, Vol 15, Pp 81-94 (2024)
The agonic line, which represents geomagnetic declinations of 0°, recently crossed the Royal Observatory of Madrid (ROM) in December 2021, causing a shift in declination values from west to east. This event constitutes a notable milestone for this s
Externí odkaz:
https://doaj.org/article/52746b0a7ad6446d9cd5f6b30bb726d0
This paper presents RAYEN, a framework to impose hard convex constraints on the output or latent variable of a neural network. RAYEN guarantees that, for any input or any weights of the network, the constraints are satisfied at all times. Compared to
Externí odkaz:
http://arxiv.org/abs/2307.08336
Autor:
Kondo, Kota, Figueroa, Reinaldo, Rached, Juan, Tordesillas, Jesus, Lusk, Parker C., How, Jonathan P.
Communication delays can be catastrophic for multiagent systems. However, most existing state-of-the-art multiagent trajectory planners assume perfect communication and therefore lack a strategy to rectify this issue in real-world environments. To ad
Externí odkaz:
http://arxiv.org/abs/2303.06222
Autor:
Kondo, Kota, Tordesillas, Jesus, Figueroa, Reinaldo, Rached, Juan, Merkel, Joseph, Lusk, Parker C., How, Jonathan P.
Although communication delays can disrupt multiagent systems, most of the existing multiagent trajectory planners lack a strategy to address this issue. State-of-the-art approaches typically assume perfect communication environments, which is hardly
Externí odkaz:
http://arxiv.org/abs/2209.13667
Autor:
Tordesillas, Jesus, How, Jonathan P.
This paper presents Deep-PANTHER, a learning-based perception-aware trajectory planner for unmanned aerial vehicles (UAVs) in dynamic environments. Given the current state of the UAV, and the predicted trajectory and size of the obstacle, Deep-PANTHE
Externí odkaz:
http://arxiv.org/abs/2209.01268
We propose a new metric called s-LID based on the concept of Local Intrinsic Dimensionality to identify and quantify hierarchies of kinematic patterns in heterogeneous media. s-LID measures how outlying a grain's motion is relative to its s nearest n
Externí odkaz:
http://arxiv.org/abs/2104.01775