Robot Person
Robot person research focuses on creating robots capable of interacting naturally and effectively with humans, encompassing tasks from simple navigation to complex manipulation and social interaction. Current research emphasizes developing robust control algorithms (like Kalman filters and Model Predictive Control), integrating advanced perception models (including Vision-Language Models and sensor fusion), and improving human-robot interaction through multimodal communication and shared autonomy. This field is significant for advancing robotics capabilities in various sectors, including healthcare, manufacturing, and service industries, by enabling robots to perform tasks more safely, efficiently, and intuitively alongside humans.
Papers
Learning robotic milling strategies based on passive variable operational space interaction control
Jamie Hathaway, Alireza Rastegarpanah, Rustam Stolkin
Two-layer adaptive trajectory tracking controller for quadruped robots on slippery terrains
Despina-Ekaterini Argiropoulos, Dimitrios Papageorgiou, Michael Maravgakis, Drosakis Drosakis, Panos Trahanias