Shared control combines human intention with autonomous decision-making. At the low level, the primary goal is to maintain safety regardless of the user's input to the system. However, existing shared control methods-based on, e.g., Model Predictive Control, Control Barrier Functions, or learning-based control-often face challenges with feasibility, scalability, and mixed constraints.
To address these challenges, we propose a Constraint-Aware Assistive Controller that computes control actions online while ensuring recursive feasibility, strict constraint satisfaction, and minimal deviation from the user's intent. It also accommodates a structured class of non-convex constraints common in real-world settings. We leverage Robust Controlled Invariant Sets for recursive feasibility and a Mixed-Integer Quadratic Programming formulation to handle non-convex constraints. We validate the approach through a large-scale user study with 66 participants-one of the most extensive in shared control research-using a simulated environment to assess task load, trust, and perceived control, in addition to performance. The results show consistent improvements across all these aspects without compromising safety and user intent. Additionally, a real-world experiment on a robotic manipulator demonstrates the framework's applicability under bounded disturbances, ensuring safety and collision-free operation.
Safety-Guaranteed Imitation Learning from Nonlinear Model Predictive Control for Spacecraft Close Proximity Operations
Space Applications Group, e:fs TechHub GmbH（空间应用组，e:fs科技枢纽 GmbH）；Faculty of Electrical Engineering and Information Technology, Ingolstadt University of Applied Sciences（电气工程与信息科技学院，因戈尔施塔特应用技术大学）
SSP: Safety-guaranteed Surgical Policy via Joint Optimization of Behavioral and Spatial Constraints
Global College, Shanghai Jiao Tong University（上海交通大学全球学院）；Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程系）；Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）
Safe Reinforcement Learning Using Advantage-Based Intervention
Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（佐治亚理工学院机器人与智能机器研究所）；Paul G. Allen School of Computer Science and Engineering, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）；Microsoft Research（微软研究院）
Barrier Bayesian Linear Regression: Online Learning of Control Barrier Conditions for Safety-Critical Control of Uncertain Systems
University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）；Technical University of Munich（慕尼黑工业大学）；University of Toronto Robotics Institute（多伦多大学机器人研究所）；Vector Institute for Artificial Intelligence（矢量人工智能研究所）
Dialogue based Interactive Explanations for Safety Decisions in Human Robot Collaboration
Department of Civil Engineering and Management, Faculty of Science and Engineering, The University of Manchester（曼彻斯特大学科学与工程学院土木工程与管理系）；VRAIN, Universitat Politècnica de València（瓦伦西亚理工大学VRAIN研究所）；Department of Engineering, University of Cambridge（剑桥大学工程系）；Department of Mechanical and Aerospace Engineering, Faculty of Science and Engineering, The University of Manchester（曼彻斯特大学科学与工程学院机械与航空航天工程系）；Center for the Possible Futures, Kyoto Institute of Technology（京都工艺纤维大学未来可能性中心）；Institute of Industrial Science, The University of Tokyo（东京大学生产技术研究所）；Graduate School of Frontier Sciences, The University of Tokyo（东京大学新领域创成科学研究科）
Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization
Centre for Perceptual and Interactive Intelligence（感知与交互智能研究中心）；The Chinese University of Hong Kong（香港中文大学）；University of California, Los Angeles（加州大学洛杉矶分校）
