Autonomous Learning for Human-scale Everyday Manipulation Tasks

Despite the fact that autonomous robotic agents performing human-scale manipulation tasks need to learn vast amounts of knowledge and many different skills the application domain receives surprisingly little attention in the area autonomous learning. On the other hand, the knowledge intensive character and the complexity of tasks as well as the desired level of performance require autonomous learning to include mechanisms that go well beyond the current state-of-the-art.

Bayesian cognitive robotics is a novel paradigm for the knowledge-enabled control of autonomous robots. The paradigm presumes that one of the most powerful ideas to equip robots with comprehensive reasoning capabilities is the lifelong autonomous learning of joint probability distributions over robot control programs, the behavior they generate and the situation-dependent effects they bring about. Having learned such probability distributions from experience, a robot can make predictions, diagnoses and perform other valuable inference tasks in order to improve its problem-solving performance.

In this talk, I will describe and discuss

• techniques for embodying methods of Bayesian cognitive robotics into modern autonomous robot control systems performing human-scale manipulation tasks in real-world settings
• learning techniques and mechanisms for scaling learning for more realistic domain sizes and producing knowledge that is applicable in perception-guided manipulation and
• methods for applying the learned knowledge to substantially improve the problem-solving capabilities and performance of robotic agents.