Robot learning from human demonstration
Teaching a Franka-Emika Panda to navigate in a cluttered environment
Local trajectory optimization is a computationally expensive task and requires the initial guess to be good enough to prevent getting stuck at local minima. This work demonstrates how kinesthetic demonstrations could be used to create a trajectory distribution using Probabilistic Movement Primitives and combine local trajectory optimization ( IROS, 2019 ) to produce smooth trajectories.