While biological inspiration has led to some of the most successful approaches in perception and machine learning -- deep neural networks, -- its deployment in real-world, safety-critical settings is yet limited. We aim to explore and critically discuss what biological inspiration in perception, learning, and decision making could bring in the future for increasing intelligence of vehicles and other robotic systems.
Thus, the aim of the workshop is to discuss potential benefits and pitfalls in applying bio-inspired approaches when developing intelligent real-world systems that perceive, interact, learn, and make decisions. We will focus on the application area of intelligent, “cognitive” vehicles and will use an unconventional format: for each of three subtopics we invited 2-4 experts from different schools of thought (for example, traditional machine learning and brain-inspired learning, conventional approach to planning and decision making and cognitive architecture-based approach, event-based bio-inspired vision and conventional machine visions, etc. ). Each speaker will give a short introductory talk followed by a moderated panel discussion around each topic. Furthermore, we will invite researchers from intelligent robotics and vehicles with a focus on perception, learning and decision making to present their work in posters and short spot-light talks.
The workshop will stimulate discussion of the role of biological inspiration in the development of future AI systems in the context of real-world, safety-critical applications of robotic systems in environments shared with humans.
- Intelligent vehicles (cars, UAVs, ...)
- Human-machine interaction
- Intelligence in the cockpit
- Robust accountable and scalable perception with neural networks and without
- Multi-modal perception and sensory integration
- Attention and cognitive control in visual and tactile perception
- Gesture recognition
- Perception for action
- Machine learning for vehicles
- Fast inference and learning
- Online learning and reliability
- Embedded machine learning
- Learning in complex hierarchical control systems
- Cognitive architectures and machine learning / neuronal networks
- Cognitive architectures for action selection
- Scalable cognitive architectures
- Learning cognitive architectures
Notification of acceptance:
Workshop day: 08.11.2019
See our call for papers for further information on the submission process.