On June 27-28, Professor Pengcheng Liu from the Department of Computer Science at the University of York, UK, visited the Institute of Hydrobiology (IHB) of the Chinese Academy of Sciences, and delivered a lecture titled "Bio-Inspired Motion Learning: From Theory to Application" as part of the Institute’s 2024 Innovation Lecture Series.

In his presentation, Prof. Liu gave a detailed overview of the research developments at the University of York and his team, with a focus on bio-inspired robotic models, shape optimization and control, long-term cognitive motion learning frameworks, and multimodal dynamic environmental perception technologies.

He also shared numerous successful case studies related to bio-inspired motion learning, intelligent control, and robotics, especially in agricultural and fisheries environments.

Following the lecture, attendees engaged in an in-depth discussion on the interdisciplinary application of robotic bio-inspired motion in smart fisheries and aquatic biodiversity conservation and utilization.

Professor Liu's intelligent control and robotics research team has established a close collaboration with the Research Group on Fisheries Intelligent Technology and Equipment at the Institute of Hydrobiology. Together, they jointly applied for and were awarded an EPSRC international cooperation project in the field of smart fisheries and environmental control. During this visit, both teams exchanged progress updates, outlined further collaborative plans in the intersection of aquatic biodiversity, intelligent technologies, and equipment, and discussed arrangements for future academic exchanges and student visits.

Prof. Liu is an Associate Professor (tenured) at the Department of Computer Science at the University of York. His research focuses on the convergence of robotics and biology. By understanding and abstracting the design principles found in biological systems, he cultivates expertise in several key areas, including modelling and transfer of human/animal excellence/knowledge for autonomous motion learning & generation, enhancement of sensory-motor and learning capabilities, optimized morphological design for behavioural variability, optimal dynamics control for motor control learning.