This paper presents the design and implementation of a Right Invariant Extended Kalman Filter (RIEKF) for estimating the states of the kinematic base of the Surena V humanoid robot. The state representation of the robot is defined on the Lie group $SE_4(3)$, encompassing the position, velocity, and orientation of the base, as well as the position of the left and right feet. In addition, we incorporated IMU biases as concatenated states within the filter.
The prediction step of the RIEKF utilizes IMU equations, while the update step incorporates forward kinematics. To evaluate the performance of the RIEKF, we conducted experiments using the Choreonoid dynamic simulation framework and compared it against a Quaternion-based Extended Kalman Filter (QEKF). The results of the analysis demonstrate that the RIEKF exhibits reduced drift in localization and achieves estimation convergence in a shorter time compared to the QEKF. These findings highlight the effectiveness of the proposed RIEKF for accurate state estimation of the kinematic base in humanoid robotics.
Kinematics-Aware Multi-Policy Reinforcement Learning for Force-Capable Humanoid Loco-Manipulation
the Robot and Artificial Intellifence Lab, College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China（机器人与人工智能实验室，电子与信息工程学院，同济大学，上海）；State Key Laboratory of Autonomous Intelligent Unmanned System(Tongji University), Shanghai, China（自主智能无人系统国家重点实验室（同济大学），上海）
Learning Differentiable Reachability Maps for Optimization-based Humanoid Motion Generation
CNRS-AIST JRL (Joint Robotics Laboratory)（法国国家科学研究中心-日本产业技术综合研究所联合机器人实验室）；IRL（智能机器人实验室）；National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究所）
Rhythm: Learning Interactive Whole-Body Control for Dual Humanoids
Fudan University（复旦大学）；TARS Robotics（TARS机器人）；Tsinghua University（清华大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai Innovation Institute（上海创新研究院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
PartiGen: Part-Aware Representations for Language-Conditioned Humanoid Motion Generation
Fudan University（复旦大学）；Shanghai Innovation Institute（上海创新研究院）；Shanghai Jiaotong University（上海交通大学）；Zhejiang University（浙江大学）；Tongji University（同济大学）；The Chinese University of Hong Kong（香港中文大学）；Humanoid Robot (Shanghai) Co., Ltd.（人形机器人（上海）有限公司）；Beihang University（北京航空航天大学）
Path and Motion Optimization for Efficient Multi-Location Inspection with Humanoid Robots
State Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence (BIGAI), Beijing（国家通用人工智能重点实验室，北京通用人工智能研究院（BIGAI））；Department of Automation, Tsinghua University（自动化系，清华大学）；Department of Automation, University of Science and Technology of China（自动化系，中国科学技术大学）；Department of Computer Science, Harbin Institute of Technology（计算机科学系，哈尔滨工业大学）；Institute for Artificial Intelligence and School of Artificial Intelligence, Peking University（人工智能研究院和人工智能学院，北京大学）
From Sign Language Generation to Humanoid Execution: Vision-Language Guided Retargeting with Collision Mitigation
Department of Systems and Control Engineering, Institute of Science Tokyo（东京科学大学系统与控制工程系）；RIKEN Guardian Robot Project（理化学研究所守护机器人项目）；ATR Hiroshi Ishiguro Laboratories（ATR石黑浩实验室）；RIKEN Center for Biosystems Dynamics Research（理化学研究所生物系统动力学研究中心）
ViLoMan: Learning Visual-Proprioceptive Whole-Body Loco-Manipulation Skills for Humanoid Robots
State Key Laboratory of AI Safety, Institute of Computing Technology, CAS（中国科学院计算技术研究所人工智能安全国家重点实验室）；University of Chinese Academy of Sciences (CAS)（中国科学院大学）；Beijing Academy of Artificial Intelligence (BAAI)（北京智源人工智能研究院）
ADP: Adversarial Dynamics Priors for Physically Grounded Humanoid Locomotion
Mechatronics, Systems and Control Lab (MSC Lab), Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院机械工程系机电一体化、系统与控制实验室（MSC实验室））；Samsung Electronics, Future Robotics AI Group（三星电子未来机器人人工智能集团）；School of Electrical Engineering, Hanyang University（汉阳大学电气工程学院）；Advanced Robotics Research Center, Korea Institute of Machinery & Materials (KIMM)（韩国机械与材料研究所先进机器人研究中心）
Proprioceptive-visual correspondence enables self-other distinction in humanoid robots
Eastern Institute of Technology, Ningbo（宁波东方理工大学）；Shanghai Jiao Tong University（上海交通大学）；Peking University（北京大学）；Carnegie Mellon University（卡内基梅隆大学）；East China Normal University（华东师范大学）；Ningbo Institute of Digital Twin（宁波数字孪生研究院）
