Zobrazeno 1 - 10
of 71 002
pro vyhledávání: '"learning and control"'
We study a system with finitely many groups of multi-action bandit processes, each of which is a Markov decision process (MDP) with finite state and action spaces and potentially different transition matrices when taking different actions. The bandit
Externí odkaz:
http://arxiv.org/abs/2412.03326
Autor:
Xu, Jianye, Alrifaee, Bassam
We propose a learning-based Control Barrier Function (CBF) to reduce conservatism in collision avoidance of car-like robots. Traditional CBFs often use Euclidean distance between robots' centers as safety margin, neglecting headings and simplifying g
Externí odkaz:
http://arxiv.org/abs/2411.08999
Deep reinforcement learning (DRL) is employed to develop control strategies for drag reduction in direct numerical simulations (DNS) of turbulent channel flows at high Reynolds numbers. The DRL agent uses near-wall streamwise velocity fluctuations as
Externí odkaz:
http://arxiv.org/abs/2501.01573
The ability to display rich facial expressions is crucial for human-like robotic heads. While manually defining such expressions is intricate, there already exist approaches to automatically learn them. In this work one such approach is applied to ev
Externí odkaz:
http://arxiv.org/abs/2412.13641
In this work, we present the design, development, and experimental validation of a custom-built quadruped robot, Ask1. The Ask1 robot shares similar morphology with the Unitree Go1, but features custom hardware components and a different control arch
Externí odkaz:
http://arxiv.org/abs/2412.08019
This paper presents a learning-based approach for centralized position control of Tendon Driven Continuum Robots (TDCRs) using Deep Reinforcement Learning (DRL), with a particular focus on the Sim-to-Real transfer of control policies. The proposed co
Externí odkaz:
http://arxiv.org/abs/2412.04829