Abstract — Human-Robot Interaction using free hand gestures is gaining more importance as more untrained humans are operating robots in home and office environments. The robot needs to solve three problems to be operated by free hand gestures: gesture (command) detection, action generation (related to the domain of the task) and association between gestures and actions. In this paper we propose a novel technique that allows the robot to solve these three problems together learning the action space, the command space, and their relations by just watching another robot operated by a human operator. The main technical contribution of this paper is the introduction of a novel algorithm that allows the robot to segment and discover patterns in its perceived signals without any prior knowledge of the number of different patterns, their occurrences or lengths. The second contribution is using a Ganger-Causality based test to limit the search space for the delay between actions and commands utilizing their relations and taking into account the autonomy level of the robot. The paper also presents a feasibility study in which the learning robot was able to predict actor’s behavior with 95.2% accuracy after monitoring a single interaction between a novice operator and a WOZ operated robot representing the actor. I.

Abstract — Research in robot navigation usually concentrates on implementing navigation algorithms that allow the robot to navigate without human aid. In many real world situations, it is desirable that the robot is able to understand natural gestures from its user or partner and use this understanding to guide its navigation. Some algorithms already exist for learning natural gestures and/or their associated actions but most of these systems does not allow the robot to automatically generate the associated controller that allows it to actually navigate in the real environment. Furthermore, a technique is needed to combine the gestures/actions learned from interacting with multiple users or partners. This paper resolves these two issues and provides a complete system that allows the robot to learn interaction protocols and act upon them using only unsupervised learning techniques and enables it to combine the protocols learned from multiple users/partners. The proposed approach is general and can be applied to other interactive tasks as well. This paper also provides a real world experiment involving 18 subjects and 72 sessions that supports the ability of the proposed system to learn the needed gestures and to improve its knowledge of different gestures and their associations to actions over time. I.

There are two functional elements used by humans to understand and perform actions. These elements are: the body schema and the body percept. The first one is a representation of the body that contains information of the body's capabilities. The second one is a snapshot of the body and its relation with the environment at a given instant. These elements interact in order to generate, among other abilities, the ability to imitate. This paper presents an approach to robot imitation based on these two functional elements. Our approach is gradually expanded throughout three developmental stages used by humans to refine imitation. Experimental results are presented to support the feasibility of the proposed approach at the current stage for 2D movements and simple manipulation actions.

Abstract — This paper introduces a similarity identification method using an Information Distance methodology. We demonstrate that this method can successfully identify the similarity and synchronicity of behavior between a human and a robot. We suggest that the application of appropriate binning strategies is the key factor that drives the effectiveness of this method. Experiments are carried out that initially validate the method on simulated data and then subsequently use real-world imitation game data. The results indicate that the method is able to correctly identify both perfectly synchronous and perfectly asynchronous imitating actions. 1