Humanoid Robot
Humanoid robots, aiming to replicate human form and function, are a focus of intense robotics research. Current efforts concentrate on improving locomotion across challenging terrains using reinforcement learning and transformer models, developing more efficient whole-body control methods (including those driven by neural signals or leveraging passive dynamics), and enhancing human-robot interaction through imitation learning and natural language processing. These advancements are significant for both the robotics community, pushing the boundaries of control algorithms and AI, and for practical applications in areas like assistive care, disaster response, and human-robot collaboration.
Papers
UKF-Based Sensor Fusion for Joint-Torque Sensorless Humanoid Robots
Ines Sorrentino, Giulio Romualdi, Daniele Pucci
Whole-body Humanoid Robot Locomotion with Human Reference
Qiang Zhang, Peter Cui, David Yan, Jingkai Sun, Yiqun Duan, Gang Han, Wen Zhao, Weining Zhang, Yijie Guo, Arthur Zhang, Renjing Xu