Offline reinforcement learning (RL) is a promising direction that allows RL agents to pre-train
on large datasets, avoiding the recurrence of expensive data collection. To advance the …

Dexterous manipulation remains an open problem in robotics. To coordinate efforts of the
research community towards tackling this problem, we propose a shared benchmark. We …

Deep neural networks (DNNs) are effective tools for learning-enabled cyber-physical
systems (CPSs) that handle high-dimensional image data. However, DNNs may make …

MicroRacer is a simple, open source environment inspired by car racing especially meant
for the didactics of Deep Reinforcement Learning. The complexity of the environment has …

Compact robotic platforms with powerful compute and actuation capabilities are key
enablers for practical, real-world deployments of multi-agent research. This article …

Robot Learning is one of the most important areas in robotics and its relevance has only
increased in more recent years. The Robot Operating System (ROS) has been one of the …

This work presents AutoDRIVE, a comprehensive research and education platform for
implementing and validating intelligent transportation algorithms pertaining to vehicular …

Recent works in end-to-end control for autonomous driving have investigated the use of
vision-based exteroceptive perception. Inspired by such results, we propose a new end-to …