Nonprehensile transportation represents a fundamental approach in robotic manipulation widely implemented in practical applications, where object dynamics constraints must be strictly maintained. However, existing approaches have certain limitations in operational reliability and control performance, particularly regarding execution efficiency and input adaptation. To address these limitations, we propose a novel shared teleoperation method for nonprehensile object transportation. The method reformulates constraints from object dynamics to robot kinematics level, eliminating the need for direct contact force control. It achieves autonomous orientation control through orientation feedforward smoothing while enabling shared position control based on user teleoperation inputs. Additionally, the coordination between position and attitude is ensured through input command optimization. The effectiveness of this method was evaluated through extensive trajectory tracking simulations and human subject experiments. The results demonstrate superiority over existing methods regarding operational safety, task efficiency, tracking accuracy, and input command adaptability.

Robots have found extremely widespread applications in today’s manufacturing industry. Integrating practical experiments for robotic measurement-machining (RMM) is crucial for cultivating academic and applied engineering professionals in the field of intelligent manufacturing. In this regard, this study proposes an integrated RMM platform for practical training of professionals in robotics. The platform features key characteristics such as modularity, customization, and an open architecture, covering the entire process of RMM, providing students with a comprehensive perspective and enhancing their interest in both theoretical learning and professional skills. The platform achieves threefold objectives: First, it is an interdisciplinary subject that allows students to translate theoretical knowledge into real-world practice. Second, it fosters critical thinking among students and enhances their ability to solve practical problems. Third, it broadens students’ horizons and motivates them to establish personal development goals through practical experience. Teaching practices have been conducted for undergraduate, graduate, and international students. The positive feedback and evaluations received confirm that this integrated RMM platform contributes to the cultivation of robotics professionals in higher engineering education.