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
Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning
Zhaoyuan Gu, Junheng Li, Wenlan Shen, Wenhao Yu, Zhaoming Xie, Stephen McCrory, Xianyi Cheng, Abdulaziz Shamsah, Robert Griffin, C. Karen Liu, Abderrahmane Kheddar, Xue Bin Peng, Yuke Zhu, Guanya Shi, Quan Nguyen, Gordon Cheng, Huijun Gao, Ye Zhao
Proposing Hierarchical Goal-Conditioned Policy Planning in Multi-Goal Reinforcement Learning
Gavin B. Rens
Talking Like One of Us: Effects of Using Regional Language in a Humanoid Social Robot
Thomas Sievers, Nele Russwinkel
Get It Right: Improving Comprehensibility with Adaptable Speech Expression of a Humanoid Service Robot
Thomas Sievers, Ralf Moeller
Project Report: Requirements for a Social Robot as an Information Provider in the Public Sector
Thomas Sievers, Nele Russwinkel