3 accepted papers
Recent advances in large language models (LLMs) have spurred interest in using these models to coordinate multi-agent robot systems. However, existing approaches often fail to handle dynamic and complex environments effectively. We present A2C-Collab, an <underline xmlns:mml="http://www.w3.org/1998/
Robotic navigation plays a pivotal role in a wide range of real-world applications. While traditional navigation systems focus on efficiency and obstacle avoidance, their inability to model complex human behaviors in shared spaces has underscored the growing need for socially aware navigation. In th
Policy learning focuses on devising strategies for agents in embodied artificial intelligence systems to perform optimal actions based on their perceived states. One of the key challenges in policy learning involves handling complex, long-horizon tasks that require managing extensive sequences of ac…