Robots can generalize manipulation skills between different scenarios by adapting to the features of the objects being manipulated. Selecting the set of relevant features for generalizing skills has usually been performed manually by a human. Alternatively, a robot can learn to select relevant features autonomously. However, feature selection usually requires a large amount of training data, which would require many demonstrations. In order to learn the relevant features more efficiently, we propose using a meta-level prior to transfer the relevance of features from previously learned skills. The experiments show that the meta-level prior more than doubles the average precision and recall of the feature selection when compared to a standard uniform prior. The proposed approach was used to learn a variety of manipulation skills, including pushing, cutting, and pouring.
SkillVLA: Tackling Combinatorial Diversity in Dual-Arm Manipulation via Skill Reuse
National University of Singapore（国立新加坡大学）；Beijing Zhongguancun Academy（北京中关村学院）；Shanghai Innovation Institute（上海创新研究院）；Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；Fudan University（复旦大学）；Smart Systems Institute, NUS（NUS智能系统研究所）
Skill-Aware Diffusion for Generalizable Robotic Manipulation
School of Control Science and Engineering, Shandong University（控制科学与工程学院，山东大学）；Department of Computer Science, The University of Manchester（计算机科学系，曼彻斯特大学）
DROM: A Language-Guided Diffusion Framework for Multi-Skill Robotic Manipulation
University of Applied Science and Arts of Southern Switzerland (SUPSI)（瑞士南部应用科学与艺术大学）；Institute of Systems and Technologies for Sustainable Production (ISTePS)（可持续生产系统与技术研究所）；Istituto Dalle Molle di studi sull’intelligenza artificiale (IDSIA)（达勒莫尔人工智能研究所）
Learning a Skill-sequence-dependent Policy for Long-horizon Manipulation Tasks
Xi’an Jiaotong-Liverpool University（西交利物浦大学）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）
Adapting Everyday Manipulation Skills to Varied Scenarios
AGH University of Science and Technology（AGH科技大学）；School of Computer Science, University of Birmingham（伯明翰大学计算机学院）；Universität Bremen（不来梅大学）；Department of Computing Science, University of Aberdeen（阿伯丁大学计算机科学系）
SkillWeave: Weaving Heterogeneous Demonstrations into Long-Horizon Manipulation Skills
Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人学院）；Keio University（庆应义塾大学）；College of Engineering, University of California, Berkeley（加州大学伯克利分校工程学院）；Bosch Center for Artificial Intelligence（博世人工智能中心）
Learning Generalizable Language-Conditioned Cloth Manipulation from Long Demonstrations
Center for Artificial Intelligence and Robotics, Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院人工智能与机器人中心）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）
SemAnCorr: Semantic Anchored Correspondence for Zero-Shot Manipulation Skill Transfer
Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；College of Connected Computing, Vanderbilt University（范德堡大学互联计算学院）；College of Engineering, University of California, Berkeley（加州大学伯克利分校工程学院）；School of Computer Science, The University of Sydney（悉尼大学计算机学院）
Abstracting Robot Manipulation Skills via Mixture-of-Experts Diffusion Policies
School of Computing, National University of Singapore（新加坡国立大学计算机学院）；Smart Systems Institute, NUS（NUS智能系统研究所）；Guangdong Institute of Intelligence Science and Technology（广东智能科学与技术研究院）；Beijing Zhongguancun Academy（北京中关村学院）
DeCo: Task Decomposition and Skill Composition for Zero-Shot Generalization in Long-Horizon 3D Manipulation
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；Huawei Noah’s Ark Lab (AI Lab), China（华为诺亚实验室（AI实验室））
ManipulationNet: An Infrastructure for Benchmarking Real-World Robot Manipulation with Physical Skill Challenges and Embodied Multimodal Reasoning
Rice University（里士大学）；U.S. National Institute of Standards and Technology（美国国家标准与技术研究院）；Massachusetts Institute of Technology（麻省理工学院）；Karlsruhe Institute of Technology（卡尔斯鲁厄技术大学）；Autodesk Research（Autodesk研究）；University of California, Berkeley（加州大学伯克利分校）；KTH Royal Institute of Technology（皇家理工学院）；Tsinghua University（清华大学）；Columbia University（哥伦比亚大学）；ASTM International（美国材料与试验协会）；Carnegie Mellon University（卡内基梅隆大学）；German Aerospace Center (DLR)（德国航空航天中心）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；NVIDIA Research（NVIDIA研究）
ARP: Enhancing Quantized Skill Abstractions via Visual Alignment and Iterative Refinement for Robotic Manipulation
School of Future Science and Engineering, Soochow University（苏州大学未来科学与工程学院）；Key Laboratory of General Artificial Intelligence and Large Models in Provincial Universities, Soochow University（苏州大学省高校通用人工智能与大模型重点实验室）；College of Mechanical and Electronic Engineering, China Jiliang University（中国计量大学机电工程学院）；College of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Leju Robotics（乐聚机器人）
Learning Forward & Reverse Skills from a Single Unfinished Demonstration for Constrained Manipulation Tasks
Autonomous Systems Lab, Technische Universität Wien (TU Wien)（自主系统实验室，维也纳工业大学（TU Wien））；Institute of Robotics and Mechatronics, German Aerospace Center (DLR)（机器人与机电一体化研究所，德国航空航天中心（DLR））
AdaptPNP: Integrating Prehensile and Non-Prehensile Skills for Adaptive Robotic Manipulation
School of Computing, National University of Singapore（新加坡国立大学计算机学院）；RoboScience（机器人科学）；East China Normal University（华东师范大学）；Peking University（北京大学）；Nanjing University（南京大学）
