10 times better.

We successfully implemented a reinforcement learning-based feedback controller that suppresses the disturbances caused by variations in the pressure of the source as well as variations caused due to production tolerances of the pump itself.

The trained controller suppresses the disturbance better than the traditional controller. This results in less peaking of the flow while the pump is in use during a medical procedure.

explore more cases.

patient ventilator asynchrony in respiratory support.

Critically ill patients who are unable to breathe (sufficiently) on their own receive respiratory support. It is essential that this support is synchronized (in time) with the patient’s spontaneous breaths.

synthetic data for bin picking.

We were asked to develop a number of datasets to aid in the development of bin-picking algorithms. Identifying suitable picking surfaces for a vacuum cup is defined by a number of relatively simple rules.

synthetic data for food sorting.

Sorting of food is inherently complex because of the great variation in food and specifically in organic produce. Machine learning is a powerful tool and very fitting to this task.