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The SNOW flagship aims to develop, integrate, demonstrate and evaluate hybrid Al capabilities for an autonomous system that can operate safely and effectively in an open world.

Within the project we develop AI that make robotic systems autonomously understand their real open world, while simultaneously plan the remainder of their operation.

  • Semantic task agreement: the system understands that it has received all information necessary to start a task and it can evaluate, from all that is known about its own capabilities and the external conditions, whether and how it is able to complete this task effectively.
  • Goal-directed perception: the system understands what new information should be obtained from the real-world, and how this information may be acquired, so that it can answer the sub-questions derived from the goal it received.

SNOW develops these AI capabilities on an actual robotic systems and evaluates the robots increase of autonomy in a real-world use-case. An increase of autonomy is achieved when fewer operator interventions are required in environments with similar complexity. SNOW keeps track of Key performance Indicators (KPI’s), such as the number of interventions in a particular environment which measurable complexity.

The quadruped robot SPOT of Boston Dynamics is, out-of-the-box, a remotely controlled system. SNOW uses the robot to evaluate the increase of autonomy of SPOT, after extension with the developed SNOW AI-modules. The SNOW-team extended SPOT in 2021 with autonomous navigation capabilities. In 2022 SPOT will be used to evaluate the increase of autonomy in an industrial environment.


  • Joris Sijs, Scientist, TNO, e-mail: