With the aim of further enabling the exploitation of intentional impacts in robotic manipulation, a control framework is presented that directly tackles the challenges posed by tracking control of robotic manipulators that are tasked to perform nominally simultaneous impacts. This framework is an extension of the reference spreading control framework, in which overlapping ante- and post-impact references that are consistent with impact dynamics are defined. In this work, such a reference is constructed starting from a teleoperation-based approach. By using the corresponding ante- and post-impact control modes in the scope of a quadratic programming control approach, peaking of the velocity error and control inputs due to impacts is avoided while maintaining high tracking performance. With the inclusion of a novel interim mode, we aim to also avoid input peaks and steps when uncertainty in the environment causes a series of unplanned single impacts to occur rather than the planned simultaneous impact. This work in particular presents for the first time an experimental evaluation of reference spreading control on a robotic setup, showcasing its robustness against uncertainty in the environment compared to three baseline control approaches.
Model-Free Large-Scale Cloth Spreading With Mobile Manipulation: Initial Feasibility Study
The Chinese University of Hong Kong（香港中文大学）；Multi-Scale Medical Robotics Center（多尺度医疗机器人中心）；Westlake University（西湖大学）；The Hong Kong University of Science and Technology（香港科技大学）
DAISS: Phase-Aware Imitation Learning for Dual-Arm Robotic Ultrasound-Guided Interventions
Chair for Computer Aided Medical Procedures(CAMP), Technical University of Munich(TUM)（计算机辅助医疗程序主席职位（CAMP），慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；The University of Hong Kong(HKU)（香港大学）
