Learning generalizable skills in robotic manipulation has long been challenging due to real-world sized observation and action spaces. One method for addressing this problem is attention focus -- the robot learns where to attend its sensors and irrelevant details are ignored. However, these methods have largely not caught on due to the difficulty of learning a good attention policy and the added partial observability induced by a narrowed window of focus. This article addresses the first issue by constraining gazes to a spatial hierarchy. For the second issue, we identify a case where the partial observability induced by attention does not prevent Q-learning from finding an optimal policy. We conclude with real-robot experiments on challenging pick-place tasks demonstrating the applicability of the approach.
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）
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）
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）
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）
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）
Decompose and Recompose: Reasoning New Skills from Existing Abilities for Cross-Task Robotic Manipulation
School of Artificial Intelligence, College of Intelligence and Computing, Tianjin University, China（人工智能学院，智能与计算学院，天津大学，中国）；School of Computer Science and Information Engineering, Hefei University of Technology, China（计算机科学与信息工程学院，合肥工业大学，中国）
本文提出Decompose and Recompose框架，通过分解现有任务演示为可解释的技能-动作对，实现跨任务机械操作的零样本泛化。
Unsupervised Skill Discovery for Robotic Manipulation through Automatic Task Generation
NAVER LABS Europe（NAVER欧洲实验室）；TU Darmstadt（达姆施塔特工业大学）；EPITA Research Laboratory (LRE)（EPITA研究实验室）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Center for Cognitive Science, TU Darmstadt（达姆施塔特工业大学认知科学中心）；Hessian Center for Artificial Intelligence (Hessian.ai)（黑森州人工智能中心）
Uni-Skill: Building Self-Evolving Skill Repository for Generalizable Robotic Manipulation
Key Laboratory of AI Safety of CAS（中国科学院人工智能安全重点实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
