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Abstract. Motions reinforce meanings in human-robot communication, when they are relevant and initiated at the right times. Given a task of using motions for an autonomous humanoid robot to communicate, different sequences of relevant motions are generated from the motion library. Each motion in the motion library is stable, but a sequence may cause the robot to be unstable and fall. We are interested in predicting if a sequence of motions will result in a fall, without executing the sequence on the robot. We contribute a novel algorithm, ProFeaSM, that uses only body angles collected during the execution of single motions and interpolations between pairs of motions, to predict whether a sequence will cause the robot to fall. We demonstrate the efficacy of ProFeaSM on the NAO humanoid robot in a real-time simulator, Webots, and on a real NAO and explore the trade-off between precision and recall.

Abstract-In the RoboCup Standard Platform League (SPL), the robot platform is the same humanoid NAO robot for all the competing teams. The NAO humanoids are fully autonomous with two onboard directional cameras, computation, multijoint body, and wireless communication among them. One of the main opportunities of having a team of robots is to have robots share information and coordinate. We address the problem of each humanoid building a model of the world in real-time, given a combination of its own limited sensing, known models of actuation, and the communicated information from its teammates. Such multi-humanoid world modeling is challenging due to the biped motion, the limited perception, and the tight coupling between behaviors, sensing, localization, and communication. We describe the real-world opportunities, constraints and limitations imposed by the NAO humanoid robots. We contribute a modeling approach that differentiates among the motion model of different objects, in terms of their dynamics, namely the static landmarks (e.g., goal posts, lines, corners), the passive moving ball, and the controlled moving robots, both teammates and adversaries. We present experimental results with the NAO humanoid robots to illustrate the impact of our multi-humanoid world modeling approach. The challenges and approaches we present are relevant to the general problem of assessing and sharing information among multiple humanoid robots acting in a world with multiple types of objects.

Abstract-We formalize the representation of gestures and present a model that is capable of synchronizing expressive and relevant gestures with text-to-speech input. A gesture consists of gesture primitives that are executed simultaneously. We formally define the gesture primitive and introduce the concept of a spatially targeted gesture primitive, i.e., a gesture primitive that is directed at a target of interest. The spatially targeted gesture primitive is useful for situations where the direction of the gesture is important for meaningful humanrobot interaction. We contribute an algorithm to determine how a spatially targeted gesture primitive is generated. We also contribute a process to analyze the input text, determine relevant gesture primitives from the input text, compose gestures from gesture primitives and rank the combinations of gestures. We propose a set of criteria that weights and ranks the combinations of gestures. Although we illustrate the utility of our model, algorithm and process using a NAO humanoid robot, our contributions are applicable to other robots.