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In this paper, we present an interactive learning and adaptation framework. The framework combines Interactive Reinforcement Learning methods to effectively adapt and refine a learned policy to cope with new users. We argue that implicit feedback provided by the primary user and guidance from a secondary user can be integrated to the adaptation mechanism, resulting at a tailored and safe interaction. We illustrate this framework with a use case in Robot Assisted Therapy, presenting a Robot Yoga Trainer that monitors a yoga training session, adjusting the session parameters based on human motion activity recognition and evaluation through depth data, to assist the user complete the session, following a Reinforcement Learning approach. CCS Concepts •Computing methodologies → Reinforcement learning; Learning from implicit feedback; Activity recognition and understanding;

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