Long of Lanzhou University

Abstract: Learning and controlling robots under system uncertainties is a fundamental yet challenging problem in robotics. Although model-free methods combined with feedforward neural networks have proved to be a recipe for success in offline settings, it’s been widely accepted that these approaches suffer from inherent limitations, including the inability to learn the Jacobian matrix, reliance on pseudo-inverse operations, and disregard for joint constraints in real-time applications. However, for practical autonomous service robots in manufacturing and disaster relief, it is crucial they can simultaneously estimate unknown structural parameters and generate optimal motions online. In this talk I will describe our recent work that establishes a unified framework for synchronous learning and control, bridging the gap between data-driven estimation and optimization-based planning. I will present three integrated technologies establishing this new capability: a Kalman filter-based algorithm for simultaneous estimation of structural parameters and optimization indices in discrete time, an acceleration-level hybrid control scheme unifying position, orientation, and force regulation through quadratic programming without matrix inversion, and a velocity-compensated gradient neural network with orthogonal projection that eliminates position errors and joint drift. At the core of these approaches is the fusion of neurodynamic optimization with real-time sensor feedback. Our methods outperform prior model-free approaches on industrial robotic arms and multi-robot platforms, demonstrating superior robustness against kinematic and dynamic uncertainties. I will also present preliminary results on distributed competitive collaboration using k-WTA networks and event-triggered planning.

Biography:

Long Jin (Senior Member, IEEE) is a Professor of Computer Science and Engineering with the School of Information Science and Engineering, Lanzhou University, Lanzhou, China, where he is also appointed as a Chair Professor with Lanzhou University of Technology. His research interests include neural networks, optimization, intelligent computing, and robotics. Prof. Jin currently serves as an Associate Editor for IEEE TIE, IEEE TFS, IEEE TASE, and IEEE/CAA Journal of Automatica Sinica.

Chair: Dr. MOHAMMED Aquil Mirza