Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …

The rise of deep learning has caused a paradigm shift in robotics research, favoring
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …

Testing the full autonomy system in simulation is the safest and most scalable way to
evaluate autonomous vehicle performance before deployment. This requires simulating …

Machine learning has long since become a keystone technology, accelerating science and
applications in a broad range of domains. Consequently, the notion of applying learning …

Integrating physics models within machine learning models holds considerable promise
toward learning robust models with improved interpretability and abilities to extrapolate. In …

Various methods for Multi-Agent Reinforcement Learning (MARL) have been developed
with the assumption that agents' policies are based on accurate state information. However …

We study off-dynamics Reinforcement Learning (RL), where the policy is trained on a source
domain and deployed to a distinct target domain. We aim to solve this problem via online …

Reinforcement learning (RL) has become a promising approach to developing controllers
for quadrupedal robots. Conventionally, an RL design for locomotion follows a position …

An agent's functionality is largely determined by its design, ie, skeletal structure and joint
attributes (eg, length, size, strength). However, finding the optimal agent design for a given …

In small batch and customized production, human-robot collaborative assembly (HRCA) is
an important method to deal with the production demand of new-energy vehicles, which …