Robotic Agent
Robotic agents are autonomous systems designed to perform tasks in the physical world, with current research heavily focused on improving their capabilities in complex and dynamic environments. Key areas of investigation include enhancing their ability to understand and execute natural language instructions, improving their physical dexterity and manipulation skills through techniques like imitation learning and reinforcement learning, and developing more robust and socially acceptable navigation strategies. These advancements are significant for advancing both fundamental robotics research and for creating more capable and useful robots for various applications, from household assistance to industrial automation.
Papers
Hazards in Daily Life? Enabling Robots to Proactively Detect and Resolve Anomalies
Zirui Song, Guangxian Ouyang, Meng Fang, Hongbin Na, Zijing Shi, Zhenhao Chen, Yujie Fu, Zeyu Zhang, Shiyu Jiang, Miao Fang, Ling Chen, Xiuying Chen
Hybrid Decision Making for Scalable Multi-Agent Navigation: Integrating Semantic Maps, Discrete Coordination, and Model Predictive Control
Koen de Vos, Elena Torta, Herman Bruyninckx, Cesar Lopez Martinez, Rene van de Molengraft