  Robots have potential to revolutionize the way we interact with the world
around us. One of their largest potentials is in the domain of mobile health
where they can be used to facilitate clinical interventions. However, to
accomplish this, robots need to have access to our private data in order to
learn from these data and improve their interaction capabilities. Furthermore,
to enhance this learning process, the knowledge sharing among multiple robot
units is the natural step forward. However, to date, there is no
well-established framework which allows for such data sharing while preserving
the privacy of the users (e.g., the hospital patients). To this end, we
introduce RoboChain - the first learning framework for secure, decentralized
and computationally efficient data and model sharing among multiple robot units
installed at multiple sites (e.g., hospitals). RoboChain builds upon and
combines the latest advances in open data access and blockchain technologies,
as well as machine learning. We illustrate this framework using the example of
a clinical intervention conducted in a private network of hospitals.
Specifically, we lay down the system architecture that allows multiple robot
units, conducting the interventions at different hospitals, to perform
efficient learning without compromising the data privacy.
