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
Fall Prediction for Bipedal Robots: The Standing Phase
M. Eva Mungai, Gokul Prabhakaran, Jessy W. Grizzle
HumanMimic: Learning Natural Locomotion and Transitions for Humanoid Robot via Wasserstein Adversarial Imitation
Annan Tang, Takuma Hiraoka, Naoki Hiraoka, Fan Shi, Kento Kawaharazuka, Kunio Kojima, Kei Okada, Masayuki Inaba
Prompt, Plan, Perform: LLM-based Humanoid Control via Quantized Imitation Learning
Jingkai Sun, Qiang Zhang, Yiqun Duan, Xiaoyang Jiang, Chong Cheng, Renjing Xu
Prototype of a robotic system to assist the learning process of English language with text-generation through DNN
Carlos Morales-Torres, Mario Campos-Soberanis, Diego Campos-Sobrino