Motor Learning and Control from Dynamical Systems Based Approach and Optimal Control (Talk)
- Jun Nakanishi
- TU Munich
Understanding the principles of natural movement generation has been and continues to be one of the most interesting and important open problems in the fields of robotics and neural control of movement. In this talk, I introduce an overview of our previous work on the control of dynamic movements in robotic systems towards the goal of control design principles and understanding of motion generation. Our research has focused in the fields of dynamical systems theory, adaptive and optimal control and statistical learning, and their application to robotics towards achieving dynamically dexterous behavior in robotic systems. First, our studies on dynamical systems based task encoding in robot brachiation, movement primitives for imitation learning, and oscillator based biped locomotion control will be presented. Then, our recent work on optimal control of robotic systems with variable stiffness actuation will be introduced towards the aim of achieving highly dynamic movements by exploiting the natural dynamics of the system. Finally, our new humanoid robot H-1 at TUM-ICS will be introduced.
Details
- 01 December 2015 • 14:00 - 15:00
- TTR, AMD Seminar Room (first floor)
- Autonomous Motion