The field of robotics has been rapidly develo** in recent years, and the work related to
training robotic agents with reinforcement learning has been a major focus of research. This …

The majority of robots in factories today are operated with conventional control strategies
that require individual programming on a task-by-task basis, with no margin for error. As an …

Increased demand for less invasive procedures has accelerated the adoption of Intraluminal
Procedures (IP) and Endovascular Interventions (EI) performed through body lumens and …

Deep Reinforcement Learning (DRL) is emerging as a promising approach to generate
adaptive behaviors for robotic platforms. However, a major drawback of using DRL is the …

Develo** generalizable manipulation skills is a core challenge in embodied AI. This
includes generalization across diverse task configurations encompassing variations in …

Deep Reinforcement Learning (DRL) has recently been focused on extracting more
knowledge from the reward signal to improve sample efficiency. The Multi-Reward …

Deep reinforcement learning has shown its effectiveness in various applications, providing a
promising direction for solving tasks with high complexity. However, naively applying …

We present a continuous perception approach that learns geometric and physical
similarities between garments by continuously observing a garment while a robot picks it up …

This study addresses the problem of hand–eye calibration in robotic systems by develo**
Continual Learning (CL)-based approaches. Traditionally, robots require explicit models to …

This paper presents the design of intelligent models and architectures for global learning-
oriented cooperation. The research work brings our current investigations towards the …