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
Reprogrammable sequencing for physically intelligent under-actuated robots
Leon M. Kamp, Mohamed Zanaty, Ahmad Zareei, Benjamin Gorissen, Robert J. Wood, Katia Bertoldi
RoVi-Aug: Robot and Viewpoint Augmentation for Cross-Embodiment Robot Learning
Lawrence Yunliang Chen, Chenfeng Xu, Karthik Dharmarajan, Zubair Irshad, Richard Cheng, Kurt Keutzer, Masayoshi Tomizuka, Quan Vuong, Ken Goldberg
PANDORA: The Open-Source, Structurally Elastic Humanoid Robot
Connor W. Herron, Alexander J. Fuge, Benjamin C. Beiter, Zachary J. Fuge, Nicholas J. Tremaroli, Stephen Welch, Maxwell Stelmack, Madeline Kogelis, Philip Hancock, Ivan Fischman Ekman Simoes, Christian Runyon, Isaac Pressgrove, Alexander Leonessa
Improving the ROS 2 Navigation Stack with Real-Time Local Costmap Updates for Agricultural Applications
Ettore Sani, Antonio Sgorbissa, Stefano Carpin