The video shows the interaction with a customized Kompa\"{i} robot. The robot consists of the Robosoft's robuLAB10 platform, tablet PC, and a Microsoft Kinect camera mounted on a pan-tilt system. A visual control algorithm provides continuous person tracking. The newly developed robot features include gesture recognition, person following, navigation with pointing, and force control, which were integrated with the Robosoft's robuBOX SDK and the Karto SLAM algorithms. The video demonstrates all the features and puts the robot in use in an everyday home scenario.

Hand-eye coordination is an important motor skill acquired in infancy which precedes pointing behavior. Pointing facilitates social interactions by directing attention of engaged participants. It is thus essential for the natural flow of human-robot interaction. Here, we attempt to explain how pointing emerges from sensorimotor learning of hand-eye coordination in a humanoid robot. During a body babbling phase with a random walk strategy, a robot learned mappings of joints for different arm postures. Arm joint configurations were used to train biologically inspired models consisting of SOMs. We show that such a model implemented on a robotic platform accounts for pointing behavior while humans present objects out of reach of the robot's hand.

This paper presents a generalized method for the design of a gesture vocabulary (GV) for intuitive and natural two-way human-robot dialog. Two GV design methodologies are proposed; one for a robot GV (RGV) and a second for a human GV (HGV). The design is based on motion gestures exerted from a cohort of subjects in response to a set of tasks needed to execute several robot waiter (RW)-customer dialogs. Using a RW setting as a case study, preliminary experimental results indicate the unique nature of the HGV obtained.