Efficient Model Learning for Human-Robot Collaborative Tasks

Understand

We present a framework for learning human user models from joint-action demonstrations that enables the robot to compute a robust policy for a collaborative task with a human.

• The learning takes place completely automatically, without any human intervention.
• First, we describe the clustering of demonstrated action sequences into different human types using an unsupervised learning algorithm.
• These demonstrated sequences are also used by the robot to learn a reward function that is representative for each type, through the employment of an inverse reinforcement learning algorithm.