Autonomous robotic systems by combining control and learning

Abstract

Thanks to the progress of control and machine learning techniques, robots nowadays are more intelligent than their predecessors 30 years ago. Advanced control techniques have enabled industrials robots to be faster, stronger, and more accurately than human workers in repetitive, structured tasks, though they are still difficult to solve everyday tasks in unstructured scenarios. Machine learning techniques have brought a revolution in perception and decision making, which allows a robot to explore new situations smartly according to its experience. However, its performance in many robotics tasks in terms of accuracy and robustness is far from being satisfactory. Control or machine learning alone is not enough for building an intelligent robot. An open problem in robotics is thus about how to combine control and machine learning in the most appropriate way in order to solve the bottleneck of applying robotics techniques in everyday life. In this talk, we will introduce our solutions to this non-trivial challenge using two challenging robotic tasks as examples: one is the autonomous robotic navigation in the dense pedestrian crowd, and the other is the deformable object manipulation for cloth assembly and suturing.