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
Automatic Gain Tuning for Humanoid Robots Walking Architectures Using Gradient-Free Optimization Techniques
Carlotta Sartore, Marco Rando, Giulio Romualdi, Cesare Molinari, Lorenzo Rosasco, Daniele Pucci
iWalker: Imperative Visual Planning for Walking Humanoid Robot
Xiao Lin, Yuhao Huang, Taimeng Fu, Xiaobin Xiong, Chen Wang
Learning Bipedal Walking for Humanoid Robots in Challenging Environments with Obstacle Avoidance
Marwan Hamze (LISV), Mitsuharu Morisawa (AIST), Eiichi Yoshida (CNRS-AIST JRL)
Achieving Stable High-Speed Locomotion for Humanoid Robots with Deep Reinforcement Learning
Xinming Zhang, Xianghui Wang, Lerong Zhang, Guodong Guo, Xiaoyu Shen, Wei Zhang
Grounding Language Models in Autonomous Loco-manipulation Tasks
Jin Wang, Nikos Tsagarakis
Online Non-linear Centroidal MPC with Stability Guarantees for Robust Locomotion of Legged Robots
Mohamed Elobaid, Giulio Turrisi, Lorenzo Rapetti, Giulio Romualdi, Stefano Dafarra, Tomohiro Kawakami, Tomohiro Chaki, Takahide Yoshiike, Claudio Semini, Daniele Pucci