Delft AI Lab Lunch with BioMorphic Lab & SensorAI Lab

About BioMorphic Intelligence Lab

Biologically inspired solutions for aerial robotics

Aerial robots are now ubiquitous. Thanks to their nimbleness, manoeuvrability, and affordability, drones are used in many sectors to monitor, map, and inspect. As a next step, flying robots offer more when interacting with their surroundings via anthropomorphic-like manipulation capabilities. Some overarching challenges remain for this new class of aerial robots, and solutions inspired by biology can be implemented across three key areas for robot performance: sensing their environment, processing this information, and acting upon the results.

SENSE | Bio-inspired perception (e.g., visual or tactile feedback) can provide the drone with information on its environment, mimicking animals’ sensory feedback. Using retina-like event cameras, drones can avoid obstacles and detect objects at a fraction of the power and latency of conventional hardware and algorithms. Enhancing tactile feedback can also prompt different behaviors in response to different force stimuli.

THINK | Bio-inspired, brain-like models from Neuromorphic AI can help lower the computational load and speed up sensory data processing for navigation. This boosts real-time control and autonomy. Compliance embedded in the control of the robot also favors safe and robust interaction with unknown environments and targets.

ACT | Bio-inspired design and materials make the drone's body fit for interaction with unknown objects and enable a safe response to external disturbances. Robot morphology can be inspired by flying animals’ shape, configuration, and materials. Together, these features create embodied intelligence and can partially offset the behavior complexity handled by the brain.

The BioMorphic Intelligence Lab aims to tackle robustness and efficiency challenges for interacting drones, using biologically inspired solutions for both the 'body' and the 'brain' and applying embodied intelligence and neuromorphic AI techniques.

About Sensor AI Lab

AI for Sensor Networks

Sensors are everywhere – measuring, processing and inferring from the environment. We also carry sensors with us personally, wherever we go. These sensors are present in smartphones and activity trackers, and provide information about where we are, how we are moving and what we are doing. Technological advances have made sensors more available and more accurate over recent years, opening up many exciting applications.

The field of sensor fusion focuses on combining data from different types of sensors in order to extract more information than that available from each sensor alone. Physical knowledge can be used, for instance about how a system can move over time or about sensor properties. AI can also be used: new models can be established using data from sensors and sensor networks. Sensor AI unites the fields of sensor fusion and AI, bringing physical knowledge into AI to enable the extraction of more information from available sensor data.

The Sensor AI Lab focuses on developing novel algorithms, and on applying these tools in different fields. Examples include human motion estimation; distributed learning in sensor networks; and navigation of swarms of multiagent systems such as robots, ships, drones and satellites.