Zobrazeno 1 - 10
of 373
pro vyhledávání: '"Youcef-toumi Kamal"'
Predicting human intent is challenging yet essential to achieving seamless Human-Robot Collaboration (HRC). Many existing approaches fail to fully exploit the inherent relationships between objects, tasks, and the human model. Current methods for pre
Externí odkaz:
http://arxiv.org/abs/2410.00302
Human intelligence possesses the ability to effectively focus on important environmental components, which enhances perception, learning, reasoning, and decision-making. Inspired by this cognitive mechanism, we introduced a novel concept termed relev
Externí odkaz:
http://arxiv.org/abs/2409.13998
Effective human-robot collaboration (HRC) requires the robots to possess human-like intelligence. Inspired by the human's cognitive ability to selectively process and filter elements in complex environments, this paper introduces a novel concept and
Externí odkaz:
http://arxiv.org/abs/2409.07753
We introduce an Invertible Symbolic Regression (ISR) method. It is a machine learning technique that generates analytical relationships between inputs and outputs of a given dataset via invertible maps (or architectures). The proposed ISR method natu
Externí odkaz:
http://arxiv.org/abs/2405.06848
In this work, we present an adjoint-based method for discovering the underlying governing partial differential equations (PDEs) given data. The idea is to consider a parameterized PDE in a general form and formulate a PDE-constrained optimization pro
Externí odkaz:
http://arxiv.org/abs/2401.17177
Perception serves as a critical component in the functionality of autonomous agents. However, the intricate relationship between perception metrics and robotic metrics remains unclear, leading to ambiguity in the development and fine-tuning of percep
Externí odkaz:
http://arxiv.org/abs/2312.07744
Human motion prediction is an essential step for efficient and safe human-robot collaboration. Current methods either purely rely on representing the human joints in some form of neural network-based architecture or use regression models offline to f
Externí odkaz:
http://arxiv.org/abs/2310.03314
We introduce MESSY estimation, a Maximum-Entropy based Stochastic and Symbolic densitY estimation method. The proposed approach recovers probability density functions symbolically from samples using moments of a Gradient flow in which the ansatz serv
Externí odkaz:
http://arxiv.org/abs/2306.04120
Identifying the mathematical relationships that best describe a dataset remains a very challenging problem in machine learning, and is known as Symbolic Regression (SR). In contrast to neural networks which are often treated as black boxes, SR attemp
Externí odkaz:
http://arxiv.org/abs/2205.15569
Publikováno v:
Reliability Engineering & System Safety, January 2023, Volume 229, 108811
While deep neural networks are highly performant and successful in a wide range of real-world problems, estimating their predictive uncertainty remains a challenging task. To address this challenge, we propose and implement a loss function for regres
Externí odkaz:
http://arxiv.org/abs/2109.08213