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Probabilistic model-based imitation learning

Peter Englert; Alexandros Paraschos; Marc Peter Deisenroth; Jan Peters
In: Adaptive Behavior, Vol. 21, No. 5, Pages 388-403, Sage Publications, 2013.


Efficient skill acquisition is crucial for creating versatile robots. One intuitive way to teach a robot new tricks is to demonstrate a task and enable the robot to imitate the demonstrated behavior. This approach is known as imitation learning. Classical methods of imitation learning, such as inverse reinforcement learning or behavioral cloning, suffer substantially from the correspondence problem when the actions (i.e. motor commands, torques or forces) of the teacher are not observed or the body of the teacher differs substantially, e.g., in the actuation. To address these drawbacks we propose to learn a robot-specific controller that directly matches robot trajectories with observed ones. We present a novel and robust probabilistic model-based approach for solving a probabilistic trajectory matching problem via policy search. For this purpose, we propose to learn a probabilistic model of the system, which we exploit for mental rehearsal of the current controller by making predictions about future trajectories. These internal simulations allow for learning a controller without permanently interacting with the real system, which results in a reduced overall interaction time. Using long-term predictions from this learned model, we train robot-specific controllers that reproduce the expert’s distribution of demonstrations without the need to observe motor commands during the demonstration. The strength of our approach is that it addresses the correspondence problem in a principled way. Our method achieves a higher learning speed than both model-based imitation learning based on dynamics motor primitives and trial-and-error-based learning systems with hand-crafted cost functions. We successfully applied our approach to imitating human behavior using a tendon-driven compliant robotic arm. Moreover, we demonstrate the generalization ability of our approach in a multi-task learning setup.

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