Zobrazeno 1 - 10
of 1 799
pro vyhledávání: '"P. Leonard, John"'
Fast and accurate physics simulation is an essential component of robot learning, where robots can explore failure scenarios that are difficult to produce in the real world and learn from unlimited on-policy data. Yet, it remains challenging to incor
Externí odkaz:
http://arxiv.org/abs/2411.00083
Autor:
Gopinath, Deepak, Cui, Xiongyi, DeCastro, Jonathan, Sumner, Emily, Costa, Jean, Yasuda, Hiroshi, Morgan, Allison, Dees, Laporsha, Chau, Sheryl, Leonard, John, Chen, Tiffany, Rosman, Guy, Balachandran, Avinash
Learning motor skills for sports or performance driving is often done with professional instruction from expert human teachers, whose availability is limited. Our goal is to enable automated teaching via a learned model that interacts with the studen
Externí odkaz:
http://arxiv.org/abs/2410.01608
This paper presents an overview of the Burer-Monteiro method (BM), a technique that has been applied to solve robot perception problems to certifiable optimality in real-time. BM is often used to solve semidefinite programming relaxations, which can
Externí odkaz:
http://arxiv.org/abs/2410.00117
We introduce SeaSplat, a method to enable real-time rendering of underwater scenes leveraging recent advances in 3D radiance fields. Underwater scenes are challenging visual environments, as rendering through a medium such as water introduces both ra
Externí odkaz:
http://arxiv.org/abs/2409.17345
Autor:
Singh, Kurran, Leonard, John J.
Underwater object-level mapping requires incorporating visual foundation models to handle the uncommon and often previously unseen object classes encountered in marine scenarios. In this work, a metric of semantic uncertainty for open-set object dete
Externí odkaz:
http://arxiv.org/abs/2409.11555
Autor:
Zhang, Yihao, Leonard, John J.
Category-level object pose and shape estimation from a single depth image has recently drawn research attention due to its wide applications in robotics and self-driving. The task is particularly challenging because the three unknowns, object pose, o
Externí odkaz:
http://arxiv.org/abs/2408.13147
Enabling robots to understand the world in terms of objects is a critical building block towards higher level autonomy. The success of foundation models in vision has created the ability to segment and identify nearly all objects in the world. Howeve
Externí odkaz:
http://arxiv.org/abs/2404.04377
Simultaneous localization and mapping (SLAM) is a critical capability in autonomous navigation, but memory and computational limits make long-term application of common SLAM techniques impractical; a robot must be able to determine what information s
Externí odkaz:
http://arxiv.org/abs/2403.19879
Despite recent advances in semantic Simultaneous Localization and Mapping (SLAM) for terrestrial and aerial applications, underwater semantic SLAM remains an open and largely unaddressed research problem due to the unique sensing modalities and the o
Externí odkaz:
http://arxiv.org/abs/2403.12837
Autor:
Lidard, Justin, So, Oswin, Zhang, Yanxia, DeCastro, Jonathan, Cui, Xiongyi, Huang, Xin, Kuo, Yen-Ling, Leonard, John, Balachandran, Avinash, Leonard, Naomi, Rosman, Guy
Interactions between road agents present a significant challenge in trajectory prediction, especially in cases involving multiple agents. Because existing diversity-aware predictors do not account for the interactive nature of multi-agent predictions
Externí odkaz:
http://arxiv.org/abs/2305.17600