Humanoid robots exhibit significant potential in executing diverse human-level skills. However, current research predominantly relies on data-driven approaches that necessitate extensive training datasets to achieve robust multimodal decision-making capabilities and generalizable visuomotor control. These methods raise concerns due to the neglect of geometric reasoning in unseen scenarios and the inefficient modeling of robot-target relationships within the training data, resulting in significant waste of training resources. To address these limitations, we present the Recurrent Geometric-prior Multimodal Policy (RGMP), an end-to-end framework that unifies geometric-semantic skill reasoning with data-efficient visuomotor control. For perception capabilities, we propose the Geometric-prior Skill Selector, which infuses geometric inductive biases into a vision language model, producing adaptive skill sequences for unseen scenes with minimal spatial common sense tuning. To achieve data-efficient robotic motion synthesis, we introduce the Adaptive Recursive Gaussian Network, which parameterizes robot-object interactions as a compact hierarchy of Gaussian processes that recursively encode multi-scale spatial relationships, yielding dexterous, data-efficient motion synthesis even from sparse demonstrations. Evaluated on both our humanoid robot and desktop dual-arm robot, the RGMP framework achieves 87% task success in generalization tests and exhibits 5x greater data efficiency than the state-of-the-art model. This performance underscores its superior cross-domain generalization, enabled by geometric-semantic reasoning and recursive-Gaussion adaptation.
Towards Learning a Generalizable 3D Scene Representation from 2D Observations
University of Hamburg - Department of Informatics（汉堡大学信息学院）；ZAL Center of Applied Aeronautical Research（应用航空研究中心）；Hamburger Informatik Technologie-Center e.V. (HITeC)（汉堡信息科技中心（HITeC））
TAGA: Terrain-aware Active Gaze Learning for Generalizable Agile Humanoid Locomotion
MarmotLab, National University of Singapore（马尔莫实验室，新加坡国立大学）；Center of X-Mechanics, Zhejiang University（浙大X力学中心）；South China University of Technology（华南理工大学）
Collision-Free Humanoid Traversal in Cluttered Indoor Scenes
Tsinghua University（清华大学）；Tongji University（同济大学）；Galbot；Shanghai Qi Zhi Institute（上海启智研究院）；South China University of Technology（华南理工大学）；Peking University（北京大学）；Zhejiang University（浙江大学）
SynAgent: Generalizable Cooperative Humanoid Manipulation via Solo-to-Cooperative Agent Synergy
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；Department of Automation, Tsinghua University（清华大学自动化系）；College of Artificial Intelligence, Nanjing Forestry University（南京林业大学人工智能学院）
EgoPriMo: Egocentric Motion Generation for Interactive Humanoid Control
Tianjin University（天津大学）；Zhongguancun Academy（中关村学院）；Beihang University（北京航空航天大学）；Zhongguancun Institute of Artificial Intelligence（中关村人工智能研究院）；DeepCybo
UniTracker: Learning Universal Whole-Body Motion Tracker for Humanoid Robots
Shanghai Jiao Tong Univeristy（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Innovation Institute（上海创新研究院）；Peking University（北京大学）；Zhejiang University（浙江大学）；Fudan University（复旦大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；ShanghaiTech University（上海科技大学）
MetaWorld-X: Hierarchical World Modeling via VLM-Orchestrated Experts for Humanoid Loco-Manipulation
University of Hamburg（汉堡大学）；School of Information Science and Technology, Beijing University of Technology（信息科学与技术学院，北京理工大学）；School of Information Science and Engineering, Fudan University（信息科学与工程学院，复旦大学）；University of Alberta（阿尔伯塔大学）
Although data-driven motion mapping methods are promising to allow intuitive robot control and teleoperation that generate human-like robot movement, they normally require tedious pair-wise training for each specific human and robot pair. This paper proposes a transferability-based mapping scheme to allow new robot and human input systems to leverage the mapping of existing trained pairs to form a mapping transfer chain, which will reduce the number of new pair-specific mappings that need to be generated. The first part of the mapping schematic is the development of a Synergy Mapping via Dual-…
ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI
University of California San Diego（加州大学圣地亚哥分校）；Carnegie Mellon University（卡内基梅隆大学）；Hillbot（Hillbot公司）；TU Dresden（德累斯顿技术大学）；Tsinghua University（清华大学）；King’s College London（伦敦国王学院）
Teacher Motion Priors: Enhancing Robot Locomotion over Challenging Terrain
University of Chinese Academy of Sciences（中国科学院大学）；Chinese Academy of Sciences（中国科学院）；Beijing Zhongke Huiling Robot Technology Co., LTD（北京中科创联机器人技术有限公司）
Let the Dynamics Flow: Stable Flow Matching Dynamical Systems
Department of Robotics, Perception, and Learning, KTH Royal Institute of Technology（机器人、感知与学习系，皇家理工学院）；Advanced Mining Technology Center (AMTC) and Department of Electrical Engineering, Universidad de Chile（先进采矿技术中心（AMTC）和电气工程系，智利大学）；Bosch Center for Artificial Intelligence, Renningen, Germany（博世人工智能中心，德国Renningen）；Italian Institute of Artificial Intelligence (AI4I), Turin, Italy（意大利人工智能研究所（AI4I），意大利都灵）
