Robot Learning

Robot Learning

The talk discusses the long-standing vision of creating autonomous robots capable of assisting humans in daily life. A crucial step toward this goal is enabling robots to learn tasks based on environmental cues or higher-level instructions. However, current learning techniques face challenges in scaling to high-dimensional manipulator or humanoid robots. The speaker presents a general… Continue reading Robot Learning

Probabilistic Logics to Neuro-Symbolic Artificial Intelligence

A central challenge to contemporary AI is to integrate learning and reasoning. The integration of learning and reasoning has been studied for decades already in the fields of statistical relational artificial intelligence and probabilistic programming. StarAI has focussed on unifying logic and probability, the two key frameworks for reasoning, and has extended this probabilistic logics… Continue reading Probabilistic Logics to Neuro-Symbolic Artificial Intelligence

Robots Learning (Through) Interactions

The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. I will discuss various learning techniques we developed that enable robots to have complex interactions with their environment and humans. Complexity arises from dealing with high-dimensional input data, non-linear dynamics in general and contacts in particular, multiple reference frames,… Continue reading Robots Learning (Through) Interactions

Intelligent Monitoring and Control of Interconnected Cyber-Physical Systems

The emergence of interconnected cyber-physical systems and sensor/actuator networks has given rise to advanced automation applications, where a large amount of sensor data is collected and processed in order to make suitable real-time decisions and to achieve the desired control objectives. However, in situations where some components behave abnormally or become faulty, this may lead… Continue reading Intelligent Monitoring and Control of Interconnected Cyber-Physical Systems