In the last bi-monthly report period we were able to assemble the Mitsubishi RV-E2 with the SCHUNK SDH Gripper (T8) and transferred the final CAD-geometry models to our VR-tool.

Fig. 1 Final geometry model of the manipulator and first test scenario

Fig. 3: Generated grasps in the new scenario all classified as force closure

As the grasp planner moves the robot only very close to the target and cannot assure predefined grasping forces, the final step to get a firm hold of an object has to be done only by the robot controller without VR-support. This functionality is realized as a service.

We tested three subjects in a target selection task based on steady state visual evoked potentials (SSVEP) while we recorded the magnetoencephalogram (MEG). In the online processing cycle we implemented the possibility to continuously retrain a classifier within a BMI-session to compensate non stationarities. This approach fosters mutual adaptation of classifier and human: the classifier learns the subject’s brain patterns while the subject learns to control the actuator using the classifier.