Zobrazeno 1 - 10
of 6 655
pro vyhledávání: '"A. Boots"'
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
A major challenge in robotics is to design robust policies which enable complex and agile behaviors in the real world. On one end of the spectrum, we have model-free reinforcement learning (MFRL), which is incredibly flexible and general but often re
Externí odkaz:
http://arxiv.org/abs/2310.04590
Robotic manipulation tasks such as object insertion typically involve interactions between object and environment, namely extrinsic contacts. Prior work on Neural Contact Fields (NCF) use intrinsic tactile sensing between gripper and object to estima
Externí odkaz:
http://arxiv.org/abs/2309.16652
We introduce LiDAR-UDA, a novel two-stage self-training-based Unsupervised Domain Adaptation (UDA) method for LiDAR segmentation. Existing self-training methods use a model trained on labeled source data to generate pseudo labels for target data and
Externí odkaz:
http://arxiv.org/abs/2309.13523