Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Triantafyllou, A"'
A novel Material Point Method (MPM) is introduced for addressing frictional contact problems. In contrast to the standard multi-velocity field approach, this method employs a penalty method to evaluate contact forces at the discretised boundaries of
Externí odkaz:
http://arxiv.org/abs/2403.13534
When Reinforcement Learning (RL) agents are deployed in practice, they might impact their environment and change its dynamics. We propose a new framework to model this phenomenon, where the current environment depends on the deployed policy as well a
Externí odkaz:
http://arxiv.org/abs/2402.09838
This work introduces a framework to address the computational complexity inherent in Mixed-Integer Programming (MIP) models by harnessing the potential of deep learning. By employing deep learning, we construct problem-specific heuristics that identi
Externí odkaz:
http://arxiv.org/abs/2401.09556
Establishing causal relationships between actions and outcomes is fundamental for accountable multi-agent decision-making. However, interpreting and quantifying agents' contributions to such relationships pose significant challenges. These challenges
Externí odkaz:
http://arxiv.org/abs/2310.11334
Responsibility attribution is a key concept of accountable multi-agent decision making. Given a sequence of actions, responsibility attribution mechanisms quantify the impact of each participating agent to the final outcome. One such popular mechanis
Externí odkaz:
http://arxiv.org/abs/2302.12676
We designed and constructed an A-sized base autonomous underwater vehicle (AUV), augmented with a stack of modular and extendable hardware and software, including autonomy, navigation, control and high fidelity simulation capabilities (A-size stands
Externí odkaz:
http://arxiv.org/abs/2212.11692
Autor:
Singla, Sumedha, Murali, Nihal, Arabshahi, Forough, Triantafyllou, Sofia, Batmanghelich, Kayhan
A highly accurate but overconfident model is ill-suited for deployment in critical applications such as healthcare and autonomous driving. The classification outcome should reflect a high uncertainty on ambiguous in-distribution samples that lie clos
Externí odkaz:
http://arxiv.org/abs/2210.12196
Autor:
Droukas, Leonidas, Doulgeri, Zoe, Tsakiridis, Nikolaos L., Triantafyllou, Dimitra, Kleitsiotis, Ioannis, Mariolis, Ioannis, Giakoumis, Dimitrios, Tzovaras, Dimitrios, Kateris, Dimitrios, Bochtis, Dionysis
Publikováno v:
Journal of Intelligent & Robotic Systems volume 107, Article number: 21 (2023)
This paper presents a comprehensive review of ground agricultural robotic systems and applications with special focus on harvesting that span research and commercial products and results, as well as their enabling technologies. The majority of litera
Externí odkaz:
http://arxiv.org/abs/2207.10457
We introduce the framework of performative reinforcement learning where the policy chosen by the learner affects the underlying reward and transition dynamics of the environment. Following the recent literature on performative prediction~\cite{Perdom
Externí odkaz:
http://arxiv.org/abs/2207.00046
Actual causality and a closely related concept of responsibility attribution are central to accountable decision making. Actual causality focuses on specific outcomes and aims to identify decisions (actions) that were critical in realizing an outcome
Externí odkaz:
http://arxiv.org/abs/2204.00302