Research Group for Neuromorphic Computing
The Research Group for Neuromorphic Computing develops advanced neural-network based algorithms, software libraries, and systems with the new generation of computing chips – brain-inspired neuromorphic sensing and computing hardware. We focus on perception, motion planning, and control for robotic actuators with applications in life sciences: healthcare, agriculture, food processing, and smart environments. We follow a human-centered design approach to develop new generation of physical AI systems that are power-efficient, adaptive, and safe.
- Neuromorphic computing hardware and algorithms
- Event-based vision
- Robotics: Motion planning, control, SLAM
- Efficient machine learning and AI
- Dynamical systems, cognitive architectures
- Assistive robotics in healthcare, agriculture, food processing, smart environments
- Machine vision in healthcare, agriculture, food processing, smart environments
- Continual learning and adaptive systems
- Robot safety, human-robot interaction
Reinforcement Learning Analysis Framework
The aim of this project is to implement a framework that facilitates the development of RL solutions for real-world applications. This is necessary since the academic literature usually focuses on specific algorithms and approaches differ widely for different regions in the highly complex RL problem space. ...
Optimizing plant health in indoor farming using reinforcement learning
The research question we tackle in this project is "How can we automatically optimize the growth path of indoor-farming plants?". To achieve this goal, we develop new algorithms to measure plant health and growth. We then evaluate reinforcement learning based approaches to optimize these metrics. ...