Zobrazeno 1 - 10
of 10 933
pro vyhledávání: '"A. ROSMAN"'
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
Autor:
DeCastro, Jonathan, Silva, Andrew, Gopinath, Deepak, Sumner, Emily, Balch, Thomas M., Dees, Laporsha, Rosman, Guy
Tight coordination is required for effective human-robot teams in domains involving fast dynamics and tactical decisions, such as multi-car racing. In such settings, robot teammates must react to cues of a human teammate's tactical objective to assis
Externí odkaz:
http://arxiv.org/abs/2410.10062
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
A number of machine learning models have been proposed with the goal of achieving systematic generalization: the ability to reason about new situations by combining aspects of previous experiences. These models leverage compositional architectures wh
Externí odkaz:
http://arxiv.org/abs/2409.14981
We represent a vehicle dynamics model for autonomous driving near the limits of handling via a multi-layer neural network. Online adaptation is desirable in order to address unseen environments. However, the model needs to adapt to new environments w
Externí odkaz:
http://arxiv.org/abs/2409.14950
Advanced Driver Assistance Systems (ADAS) alert drivers during safety-critical scenarios but often provide superfluous alerts due to a lack of consideration for drivers' knowledge or scene awareness. Modeling these aspects together in a data-driven w
Externí odkaz:
http://arxiv.org/abs/2409.04738
Autor:
Tonja, Atnafu Lambebo, Dossou, Bonaventure F. P., Ojo, Jessica, Rajab, Jenalea, Thior, Fadel, Wairagala, Eric Peter, Aremu, Anuoluwapo, Moiloa, Pelonomi, Abbott, Jade, Marivate, Vukosi, Rosman, Benjamin
High-resource language models often fall short in the African context, where there is a critical need for models that are efficient, accessible, and locally relevant, even amidst significant computing and data constraints. This paper introduces Inkub
Externí odkaz:
http://arxiv.org/abs/2408.17024
Reinforcement learning (RL) has progressed substantially over the past decade, with much of this progress being driven by benchmarks. Many benchmarks are focused on video or board games, and a large number of robotics benchmarks lack diversity and re
Externí odkaz:
http://arxiv.org/abs/2407.14516
Autor:
Moodley, Perusha, Kaushik, Pramod, Thambi, Dhillu, Trovinger, Mark, Paruchuri, Praveen, Hong, Xia, Rosman, Benjamin
Decision Transformers, in their vanilla form, struggle to perform on image-based environments with multi-discrete action spaces. Although enhanced Decision Transformer architectures have been developed to improve performance, these methods have not s
Externí odkaz:
http://arxiv.org/abs/2407.01310
Autor:
Hu, Haimin, DeCastro, Jonathan, Gopinath, Deepak, Rosman, Guy, Leonard, Naomi Ehrich, Fisac, Jaime Fernández
Non-cooperative interactions commonly occur in multi-agent scenarios such as car racing, where an ego vehicle can choose to overtake the rival, or stay behind it until a safe overtaking "corridor" opens. While an expert human can do well at making su
Externí odkaz:
http://arxiv.org/abs/2406.09810