Primitives learning

Analysis of the human movements for a possible robot motion planning. As input the real time skeleton of the human detected by the Human Tracking Module and depending on the position of the joints of the skeleton returns as output the recognized gesture.

It provides the robots reactive and efficient decision-making capabilities to adapt to highly unstructured environments, as the ones in which the human operator might constantly enter in and modify the workspace. In a situation in which an operator enters in the workspace, the robot should be able to efficiently recompute its future actions to safely adapt to the human. To do so, a set of algorithms have been developed for human-aware task and motion planning. To safely adapt the robot motions to humans, dynamically adaptable, human-aware, motion planning algorithm has been developed, in collaboration with CNR. The algorithm has two main elements. On one hand, a human occupancy prediction algorithm has been developed. Given the current human state, the algorithm predicts the probability density function on where the human will be in the future. Then, the motion planning algorithm, developed by CNR, takes this information into consideration to plan safe motions in the workspace. The problem associated with modelling and learning the human occupancy prediction modules has been addressed. Based on highly expressive probability density function models, known as Normalizing Flows; in our work, we explored the integration of these models for the problem of learning conditioned human occupancy predicted densities. Additionally, we have explored the possibility of using previous data to guide the search in motion planning and improve its efficiency. By using CNN and ANN as approximators, we trained predictors on the feasibility of some key decisions in robot grasping and further derived heuristics from those predictors to accelerate motion planning.