Social robots that physically display emotion invite natural communication with their human interlocutors, enabling applications like robot-assisted therapy where a complex robot's breathing influences human emotional and physiological state. Using DIY fabrication and assembly, we explore how simple 1-DOF robots can express affect with economy and user customizability, leveraging open-source designs.We developed low-cost techniques for coupled iteration of a simple robot's body and behaviour, and evaluated its potential to display emotion. Through two user studies, we (1) validated these CuddleBits' ability to express emotions (N=20);(2) sourced a corpus of 72 robot emotion behaviours from participants (N=10); and (3) analyzed it to link underlying parameters to emotional perception (N=14).We found that CuddleBits can express arousal (activation), and to a lesser degree valence (pleasantness). We also show how a sketch-refine paradigm combined with DIY fabrication and novel input methods enable parametric design of physical emotion display, and discuss how mastering this parsimonious case can give insight into layering simple behaviours in more complex robots.

Advances in the field of touch recognition could open up applications for touch-based interaction in areas such as Human-Robot Interaction (HRI). We extended this challenge to the research community working on multimodal interaction with the goal of sparking interest in the touch modality and to promote exploration of the use of data processing techniques from other more mature modalities for touch recognition. Two data sets were made available containing labeled pressure sensor data of social touch gestures that were performed by touching a touch-sensitive surface with the hand. Each set was collected from similar sensor grids, but under conditions reflecting different application orientations: CoST: Corpus of Social Touch and HAART: The Human-Animal Affective Robot Touch gesture set. In this paper we describe the challenge protocol and summarize the results from the touch challenge hosted in conjunction with the 2015 ACM International Conference on Multimodal Interaction (ICMI). The most important outcomes of the challenges were: (1) transferring techniques from other modalities, such as image processing, speech, and human action recognition provided valuable feature sets; (2) gesture classification confusions were similar despite the various data processing methods used.

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