Department of Computer, Control and Management Engineering

Abstract:

In this talk, I will present my research on enhancing robotic motion and control across diverse and challenging scenarios.
First, I will explore the generation of optimal jumping motions using Reinforcement Learning (RL), integrating physical insights to guide the learning process.
This significantly reduces the training time compared to standard end-to-end approaches.
Second, I will illustrate the development of novel robotic platforms that combine ropes and legged systems to address the challenges of maintenance in remote, hazardous environments. By leveraging optimal control techniques, I designed a computationally efficient planning algorithm for precise and adaptive jumping motions.