Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI)
technique. However, current studies and applications need to address its scalability, non …

The burgeoning fields of robot learning and embodied AI have triggered an increasing
demand for large quantities of data. However, collecting sufficient unbiased data from the …

The ability to reuse collected data and transfer trained policies between robots could
alleviate the burden of additional data collection and training. While existing approaches …

Significant progress has been made in enhancing the motion capabilities of quadruped
robots in unstructured environments due to advancements in hardware and control …

Over recent decades, sequential decision-making tasks are mostly tackled with expert
systems and reinforcement learning. However, these methods are still incapable of being …

Reinforcement learning is able to obtain generalized low-level robot policies on diverse
robotics datasets in embodied learning scenarios, and Transformer has been widely used to …

The collective performance or capacity of collaborative autonomous systems such as a
swarm of robots is jointly influenced by the morphology and the behavior of individual …

Designing generalizable agents capable of adapting to diverse embodiments has achieved
significant attention in Reinforcement Learning (RL), which is critical for deploying RL …

Multiagent Reinforcement Learning (MARL) plays a pivotal role in intelligent vehicle
systems, offering solutions for complex decision-making, coordination, and adaptive …

Decision Transformers (DTs) have been highly effective for offline reinforcement learning
(RL) tasks, successfully modeling the sequences of actions in a given set of demonstrations …