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One of the most important factors for infants’ learning is positive and negative feedback from their caregiver. In a similar way, learning robots can also use the feedback from their user as a basis for learning and adapting to the user’s preferences. A popular example of learning through user feedback is reinforcement learning with a human teacher (Thomaz & Brezeal, 2006), but many applications of learning by positive and negative examples with assistance from a user or behavior adaptation and refinement (Kim & Scassellati, 2007) require an understanding of the user’s expression of approval and disapproval. This paper focuses on enabling a robot to learn to understand natural, multimodal approving or disapproving feedback given in response to the robot's moves. Humans express approval and disapproval toward a robot through different channels, such as words, prosody, gestures, facial expressions and touch. Most work on understanding approval and disapproval has been done with single-modal approaches based on prosodic information from speech signals such as intonation, pitch, tempo, loudness and rhythm (Breazeal, 2002) (Kim & Scassellati, 2007). However, we assume that integrating multiple modalities improves the reliability of the recognition and allows the system to adapt to the individual preferences of the user. We determined the modalities to implement in our system through a user-study. It is described in detail in (Austermann & Yamada, 2008). We found, that speech was by far the most frequently used modality, when giving feedback to an AIBO robot. 78.37% of all feedback was given by speech. It was followed by touch, which was used for 20.92% of the feedbacks. Gesture was applied for giving instructions, but did not play a significant role for giving feedback and was only used in 0.71% of the cases. Therefore, in addition to prosody, we focus on the contents of the speech utterances as well as on interaction through the touch sensors of the robot. We did not integrate the recognition of facial expressions, because we wanted the users to move around freely and interact naturally. A facial expression recognizer would have restricted the users' movements by requiring them to look straight into a camera. In order to learn to interpret user feedback, our system utilizes a biologically-inspired twostaged learning method which is modeled after basic learning processes in humans and

Learning to Understand Expressions of Approval and Disapproval through Game-Based Training Tasks