Adaptive behavior by multivariate learning and supervision processes for mobile robots – MUSCAA

Mobile robots are more and more efficient but limited by the problem of
variation of motion properties, as is the case for applications in
natural environments. Sharp transitions can be estimated reactively,
but are difficult to predict, and lack of anticipation can lead to
inappropriate or even hazardous behaviors. This project aims to overcome
this problem by proposing adaptive mechanisms for robotic behavior
by anticipating these variations from scene perception. The project
proposes to develop machine learning approaches to predict and map the
interaction conditions. It will also develop stable supervision
processes to select and modify on-line several control modes. Tested on
realistic scenarios using the robotic platforms available from the
project members, such developments will strengthen the autonomy of
robots to offer efficient and safe solutions to societal issues,
particularly for agriculture.