Zobrazeno 1 - 10
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pro vyhledávání: '"Boots A"'
We investigate the problem of teaching a robot manipulator to perform dynamic non-prehensile object transport, also known as the `robot waiter' task, from a limited set of real-world demonstrations. We propose an approach that combines batch reinforc
Externí odkaz:
http://arxiv.org/abs/2412.00086
Autor:
Higuera, Carolina, Sharma, Akash, Bodduluri, Chaithanya Krishna, Fan, Taosha, Lancaster, Patrick, Kalakrishnan, Mrinal, Kaess, Michael, Boots, Byron, Lambeta, Mike, Wu, Tingfan, Mukadam, Mustafa
In this work, we introduce general purpose touch representations for the increasingly accessible class of vision-based tactile sensors. Such sensors have led to many recent advances in robot manipulation as they markedly complement vision, yet soluti
Externí odkaz:
http://arxiv.org/abs/2410.24090
Autor:
Wagenmaker, Andrew, Huang, Kevin, Ke, Liyiming, Boots, Byron, Jamieson, Kevin, Gupta, Abhishek
In order to mitigate the sample complexity of real-world reinforcement learning, common practice is to first train a policy in a simulator where samples are cheap, and then deploy this policy in the real world, with the hope that it generalizes effec
Externí odkaz:
http://arxiv.org/abs/2410.20254
Autor:
Yang, Yuxiang, Shi, Guanya, Lin, Changyi, Meng, Xiangyun, Scalise, Rosario, Castro, Mateo Guaman, Yu, Wenhao, Zhang, Tingnan, Zhao, Ding, Tan, Jie, Boots, Byron
We focus on agile, continuous, and terrain-adaptive jumping of quadrupedal robots in discontinuous terrains such as stairs and stepping stones. Unlike single-step jumping, continuous jumping requires accurately executing highly dynamic motions over l
Externí odkaz:
http://arxiv.org/abs/2409.10923
Current developments in autonomous off-road driving are steadily increasing performance through higher speeds and more challenging, unstructured environments. However, this operating regime subjects the vehicle to larger inertial effects, where consi
Externí odkaz:
http://arxiv.org/abs/2405.16487
Autor:
Lin, Changyi, Liu, Xingyu, Yang, Yuxiang, Niu, Yaru, Yu, Wenhao, Zhang, Tingnan, Tan, Jie, Boots, Byron, Zhao, Ding
Quadrupedal robots have emerged as versatile agents capable of locomoting and manipulating in complex environments. Traditional designs typically rely on the robot's inherent body parts or incorporate top-mounted arms for manipulation tasks. However,
Externí odkaz:
http://arxiv.org/abs/2403.18197
We focus on the problem of long-range dynamic replanning for off-road autonomous vehicles, where a robot plans paths through a previously unobserved environment while continuously receiving noisy local observations. An effective approach for planning
Externí odkaz:
http://arxiv.org/abs/2403.11298
Reliable estimation of terrain traversability is critical for the successful deployment of autonomous systems in wild, outdoor environments. Given the lack of large-scale annotated datasets for off-road navigation, strictly-supervised learning approa
Externí odkaz:
http://arxiv.org/abs/2312.16016
Terrain traversability in unstructured off-road autonomy has traditionally relied on semantic classification, resource-intensive dynamics models, or purely geometry-based methods to predict vehicle-terrain interactions. While inconsequential at low s
Externí odkaz:
http://arxiv.org/abs/2311.12284
Precise arbitrary trajectory tracking for quadrotors is challenging due to unknown nonlinear dynamics, trajectory infeasibility, and actuation limits. To tackle these challenges, we present Deep Adaptive Trajectory Tracking (DATT), a learning-based a
Externí odkaz:
http://arxiv.org/abs/2310.09053