Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

Robotic equipment has been playing a central role since the proposal of smart
manufacturing. Since the beginning of the first integration of industrial robots into production …

Path Following and Collision Avoidance, be it for unmanned surface vessels or other
autonomous vehicles, are two fundamental guidance problems in robotics. For many …

Theory of mind (ToM) is the psychological construct by which we model another's internal
mental states. Through ToM, we adjust our own behaviour to best suit a social context, and …

This paper presents LEMURS, an algorithm for learning scalable multi-robot control policies
from cooperative task demonstrations. We propose a port-Hamiltonian description of the …

Despite recent advances in safe reinforcement learning (RL), safety constraints are often
violated at deployment, especially under extreme uncertainty in memory-based partially …

We present Bayesian Team Imitation Learner (BTIL), an imitation learning algorithm to
model the behavior of teams performing sequential tasks in Markovian domains. In contrast …

A known modality for imitation learning in robotics [15] is to employ a sensor suite to learn
from observing (LfO) the expert. The sensed data constitutes a trajectory, usually modeled …

An important real-world application of multi-robot systems is multi-robot patrolling (MRP),
where robots must carry out the activity of going through an area at regular intervals …

Advances in machine learning technologies in recent years have facilitated developments in
autonomous robotic systems. Designing these autonomous systems typically requires …