Robotgenskill resulted in novel invariant representations of motions and interaction force data, and in novel procedures to calculate these representations in a robust and efficient way. Furthermore, we showed how these invariant representations can be used to extract intrinsic features of the demonstrated task, and to express these features as constraints which, together, constitute the skill model. Embedded in an optimal control scheme, this learned skill model is then connected to a real-time robot controller which performs the task in a new situation, while adapting the task execution to sensor information.
We used this methodology to generate shape-preserving and reactive robot trajectories in free space, towards new and even moving targets. Building upon this, we showed that virtual demonstrations can be generated and added to human demonstrations to extend the extrapolation capabilities of robot motion models that are learned from demonstrations. This approach allowed us to drastically reduce the required number of human demonstrations, hence reducing the cost of commissioning robot tasks, while improving the robustness, reliability, explainability and hence also safety of the robot application.
For robot skills involving motion in contact, we developed a generic methodology based on similar concepts and procedures to extract a task model, but now also considering interaction forces. The skill model enables adaptation of the task execution to deal with changes between the demonstration context and the new task context. We validated the method for seven different applications including assembly tasks, surface following tasks and manipulation of articulated objects such as doors and drawers.
As for the biomechanics applications, we designed an optimal method for estimating the average axis of a human joint (e.g. the knee axis), with improved sensitivity to noise and including a dispersion analysis of the instantaneous knee axis. We also designed a novel procedure for estimating the knee flexion angle based on functionally defined coordinate systems. This procedure eliminates the need for a time-consuming calibration of the markers used for motion capture, which is important for clinical practice.
An unplanned outcome was the new numerical solver for Optimal Control Problems, referred to as FATROP, which achieves the same level of robustness as state-of-the-art generic nonlinear solvers such as IPOPT, but with a much smaller computation time. Since FATROP can efficiently solve a broad class of nonlinear optimal control problems, it is expected to impact other domains beyond the applications considered in the Robotgenskill project, such as drones, self-driving cars and machines like gantries, compressors and drivetrains.
