Automatic assembly has broad applications in industries. Traditional assembly tasks utilize predefined trajectories or tuned force control parameters, which make the automatic assembly time-consuming, difficult to generalize, and not robust to uncertainties. In this paper, we propose a learning framework for high precision industrial assembly. The framework combines both the supervised learning and the reinforcement learning. The supervised learning utilizes trajectory optimization to provide the initial guidance to the policy, while the reinforcement learning utilizes actor-critic algorithm to establish the evaluation system even the supervisor is not accurate. The proposed learning framework is more efficient compared with the reinforcement learning and achieves better stability performance than the supervised learning. The effectiveness of the method is verified by both the simulation and experiment.
Design And Control of A Robotic Arm For Industrial Applications
CMR Technical Campus（CMR技术学院）；JNTUH University College of Engineering, Science & Technology Hyderabad（贾瓦哈拉尔·尼赫鲁科技大学海得拉巴工程科学与技术学院）；Holy Mary Institute of Technology（圣玛丽技术学院）
Context-Based Meta Reinforcement Learning for Robust and Adaptable Peg-in-Hole Assembly Tasks
Humanoid Robots Lab and Center for Robotics, University of Bonn, Germany（波恩大学人形机器人实验室和机器人中心）；Lamarr Institute for Machine Learning and Artificial Intelligence, Bonn, Germany（波恩大学拉马尔人工智能与机器学习研究所）；Cyber Physical Systems Lab, Egypt Japan University of Science and Technology, Alexandria, Egypt（埃及日本科学技术大学网络物理系统实验室）；Faculty of Engineering, Alexandria University, Alexandria, Egypt（亚历山大大学工程学院）；Mechatronics Department, Ain Shams University, Cairo, Egypt（亚历山大大学机电系）
AssemblyBench: Physics-Aware Assembly of Complex Industrial Objects
Rutgers, The State University of New Jersey（新泽西罗格斯大学）；The Australian National University（澳大利亚国立大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔兰根-纽伦堡弗里德里希-亚历山大大学）；Mitsubishi Electric Research Laboratories (MERL)（三菱电机研究实验室）
RoCo Challenge at AAAI 2026: Benchmarking Robotic Collaborative Manipulation for Assembly Towards Industrial Automation
Nanyang Technological University, Singapore（南洋理工大学，新加坡）；Beijing University of Posts and Telecommunications, Beijing, China（北京邮电大学，北京，中国）；Carnegie Mellon University, Pittsburgh, USA（卡内基梅隆大学，匹兹堡，美国）；Agency for Science, Technology and Research (A*STAR), Singapore（科技研究局（A*STAR），新加坡）
EUPHORIA: Efficient Universal Planning via Hybrid Optimization for Robust Industrial Robotic Assembly
National Taiwan University（国立台湾大学）；MoonShine Animation Studio（MoonShine动画工作室）；National Cheng Kung University（国立成功大学）；The University of British Columbia（不列颠哥伦比亚大学）；National Yang Ming Chiao Tung University（阳明交通大学）
A Framework for Closed-Loop Robotic Assembly, Alignment and Self-Recovery of Precision Optical Systems
Research Laboratory of Electronics, Massachusetts Institute of Technology（麻省理工学院电子研究实验室）；NSF Institute for Artificial Intelligence and Fundamental Interactions (IAIFI) and the Department of Physics, Massachusetts Institute of Technology（国家科学基金会人工智能与基本相互作用研究所（IAIFI）和麻省理工学院物理系）；Nokia Bell Labs（诺基亚贝尔实验室）；School of Electrical, Computing and Energy Engineering, Arizona State University（亚利桑那州立大学电气、计算与能源工程学院）
Precision in Practice: Knowledge Guided Code Summarizing Grounded in Industrial Expectations
School of Computer Science, Wuhan University, China（武汉大学计算机科学学院）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, China（香港科学与技术大学计算机科学与工程系）；Department of Computer Science（计算机科学系）
