Behavioural Repertoire via Generative Adversarial Policy Networks

Understand

Learning algorithms are enabling robots to solve increasingly challenging real-world tasks.

• These approaches often rely on demonstrations and reproduce the behavior shown.
• Unexpected changes in the environment may require using different behaviors to achieve the same effect, for instance to reach and grasp an object in changing clutter.
• An emerging paradigm addressing this robustness issue is to learn a diverse set of successful behaviors for a given task, from which a robot can select the most suitable policy when faced with a new environment.