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
Walk along: An Experiment on Controlling the Mobile Robot 'Spot' with Voice and Gestures
Renchi Zhang, Jesse van der Linden, Dimitra Dodou, Harleigh Seyffert, Yke Bauke Eisma, Joost C. F. de Winter
Learning Rapid Turning, Aerial Reorientation, and Balancing using Manipulator as a Tail
Insung Yang, Jemin Hwangbo
Position and Altitude of the Nao Camera Head from Two Points on the Soccer Field plus the Gravitational Direction
Stijn Oomes, Arnoud Visser
Development of a semi-autonomous framework for NDT inspection with a tilting aerial platform
Salvatore Marcellini, Simone D'Angelo, Alessandro De Crescenzo, Michele Marolla, Vincenzo Lippiello, Bruno Siciliano
Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models
Annie S. Chen, Alec M. Lessing, Andy Tang, Govind Chada, Laura Smith, Sergey Levine, Chelsea Finn
MARLIN: A Cloud Integrated Robotic Solution to Support Intralogistics in Retail
Dennis Mronga, Andreas Bresser, Fabian Maas, Adrian Danzglock, Simon Stelter, Alina Hawkin, Hoang Giang Nguyen, Michael Beetz, Frank Kirchner