Kristína Malinovská

Self-Supervised Bio-Inspired Robotic Trajectory Planning with Obstacle Avoidance

This follow-up work tests the feasibility of the neuro-inspired self-supervised learning framework for trajectory planning that leverages forward and inverse models as the internal supervisory mechanism in an environment that contains an obstacle, and demonstrates the tendency of the planner to exploit the learning signal provided by the forward and inverse models.