Programming robot behavior in a complex world faces challenges on multiple levels, from
dextrous low-level skills to high-level planning and reasoning. Recent pre-trained Large …

This paper provides a comprehensive review of the integration of advanced power
management systems and learning techniques in the field of robotics. It identifies the critical …

Large language models (LLMs) exhibit outstanding reasoning abilities when robots perform
tasks, but are limited to the passively receiving information and lack multi-feature perception …

Explaining the behaviour of intelligent agents learned by reinforcement learning (RL) to
humans is challenging yet crucial due to their incomprehensible proprioceptive states …

To expedite the development process of interactive reinforcement learning (IntRL)
algorithms, prior work often uses perfect oracles as simulated human teachers to furnish …

The up-and-coming concept of Industry 5.0 fore-sees human-centric flexible production
lines, where collaborative robots support human workforce. In order to allow a seamless …

This study contributes to the evolving field of robot learning in interaction with humans,
examining the impact of diverse input modalities on learning outcomes. It introduces the …

Active perception enables robots to dynamically gather information by adjusting their
viewpoints, a crucial capability for interacting with complex, partially observable …

It is undeniable that the development of humanity and technology has been closely
connected for decades. What was important before is no longer so. This is because, given …

This position paper calls for an increase in the number and flexibility of input mechanisms in
user-robot interactions, highlighting their potential to enhance learning algorithms through …