Primary-Fine Decoupling for Action Generation in Robotic Imitation
MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China（脑启发智能感知与认知国家重点实验室，中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（人工智能研究院，合肥综合性国家科学中心）
Xiaohan Lei, Min Wang, Wengang Zhou, Xingyu Lu, Houqiang Li
Multi-modal distribution in robotic manipulation action sequences poses critical challenges for imitation learning. To this end, existing approaches often model the action space as either a discrete set of tokens or a continuous, latent-variable distribution. However, both approaches present trade-offs: some methods discretize actions into tokens and therefore lose fine-grained action variations, while others generate continuous actions in a single stage tend to produce unstable mode transitions. To address these limitations, we propose Primary-Fine Decoupling for Action Generation (PF-DAG), a two-stage framework that decouples coarse action consistency from fine-grained variations. First, we compress action chunks into a small set of discrete modes, enabling a lightweight policy to select consistent coarse modes and avoid mode bouncing. Second, a mode conditioned MeanFlow policy is learned to generate high-fidelity continuous actions. Theoretically, we prove PF-DAG's two-stage design achieves a strictly lower MSE bound than single-stage generative policies. Empirically, PF-DAG outperforms state-of-the-art baselines across 56 tasks from Adroit, DexArt, and MetaWorld benchmarks. It further generalizes to real-world tactile dexterous manipulation tasks. Our work demonstrates that explicit mode-level decoupling enables both robust multi-modal modeling and reactive closed-loop control for robotic manipulation.
Dynamics-Aware Meta-Imitation for Generalization to Unseen Robotic Manipulation
State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所机器人学国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Shenyang University of Technology（沈阳工业大学）
College of Engineering and Physical Sciences, Aston University（工程与物理科学学院，阿斯顿大学）；Department of Computer Science Department, Loughborough University（计算机科学系，洛桑大学）
FoAM: Foresight-Augmented Multi-Task Imitation Policy for Robotic Manipulation
University of Southern California（南加州大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）
ALPHA-$α$ and Bi-ACT Are All You Need: Importance of Position and Force Information/Control for Imitation Learning of Unimanual and Bimanual Robotic Manipulation with Low-Cost System
Osaka University（大阪大学）；Graduate School of Information Science and Technology of Osaka University（大阪大学信息科学与技术研究生院）；School of Engineering Science, Osaka University（大阪大学工程科学学院）
OminiAdapt: Learning Cross-Task Invariance for Robust and Environment-Aware Robotic Manipulation
University of Science and Technology of China（中国科学技术大学）；Beijing Institute of Technology（北京理工大学）；Institute of Automation Chinese Academy of Sciences（中国科学院自动化研究所）
Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Hong Kong University of Science and Technology（香港科技大学）
Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Hong Kong University of Science and Technology（香港科技大学）；University of Macau（澳门大学）
A Wearable Robotic Hand for Hand-over-Hand Imitation Learning
Tsinghua-Berkeley Shenzhen Institute, Tsinghua University（清华大学清华-伯克利深圳学院）；Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；Shanghai AI Lab（上海人工智能实验室）；Shanghai Qi Zhi Institute（上海期智研究院）
RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulation Environments
Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST)（日本国家先进工业科学技术研究院人工智能研究中心）；CNRS-AIST JRL (Joint Robotics Laboratory), IRL（法国国家科学研究中心与日本AIST联合机器人实验室）；Institute for AI and Robotics, Future Robotics Organization, Waseda University（早稻田大学未来机器人组织人工智能与机器人研究所）；AI Robot Association (AIRoA)（人工智能机器人协会）
Northeastern University（东北大学）；Faculty of Robot Science and Engineering, Northeastern University（东北大学机器人科学与工程学院）；College of Information Science and Engineering, Northeastern University（东北大学信息科学与工程学院）；Foshan Graduate School, Northeastern University（东北大学佛山研究生院）
DAISS: Phase-Aware Imitation Learning for Dual-Arm Robotic Ultrasound-Guided Interventions
Chair for Computer Aided Medical Procedures(CAMP), Technical University of Munich(TUM)（计算机辅助医疗程序主席职位（CAMP），慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；The University of Hong Kong(HKU)（香港大学）
Robotic Manipulation via Imitation Learning: Taxonomy, Evolution, Benchmark, and Challenges
Ecole Centrale de Lyon, CNRS, Universite Claude Bernard Lyon 1, INSA Lyon, Université Lumière Lyon 2, LIRIS, UMR5205（Lyon中央理工学院、CNRS、Claude Bernard Lyon 1大学、INSA Lyon、Lyon大学、LIRIS、UMR5205）；Beihang University（北航大学）；Dalian University of Technology（大连理工大学）；Institut Universitaire de France (IUF)（法国大学研究院（IUF））
Learning Beyond Rigid Manipulation with Hybrid Soft-Rigid Robots
Cognitive Robotics, Delft University of Technology（德鲁特理工大学认知机器人学）；Bioinspired Soft Robotics Lab, Istituto Italiano di Tecnologia（意大利技术研究院仿生软机器人实验室）；Technology Innovation Institute (TII)（技术创新研究所）；Institute of Robotics and Mechatronics, German Aerospace Center (DLR)（德国航空航天中心机器人与机电研究所）
Gaze-Guided Task Decomposition for Imitation Learning in Robotic Manipulation
The University of Tokyo（东京大学）；Graduate School of Information Science and Technology（信息科学与技术研究生院）；Laboratory for Intelligent Systems and Informatics（智能系统与信息学实验室）
