Since the appearance of humanoid robots is similar with real human, a method is proposed to discriminate these robots from real human in a perception sensor network (PSN) which uses multiple Kinects and PTZ cameras to provide the location and name of tracked human to these robots. To this end, the global map which is acquired by fusing and calibrating one local map built by a robot and the other local map by the PSN system is used to provide the location of the robot. Since the PSN updates periodically the location of the robot by subscribing the ROS (Robot Operating System) topics, the robot which is wrongly detected as human but yields the closest distance to the location can be correctly identified as a robot in the system. Therefore, the misunderstood cases when robots are recognized as human in the PSN system are removed effectively. The experimental results demonstrate the outperforming of the proposed method in various scenarios.

Social skills training of children with autism spectrum disorders (ASD) is not an easy problem enough to typically spend a lot of time and repetitive efforts. As means of assistance, we propose and examine the feasibility of a robotic behavioral intervention system to facilitate social development of the children with ASD and relieve operational burden during training process. To this end, based on well-known behavioral treatment protocols, the robot system incorporates robotic stimulation design, recognition modules for human activities, and reinforcement procedure in the interaction scheme design. Using these configuration, it repeatedly perform a role of training eye contact and reading emotions targeted at preschoolers with a high functioning level in the planned training process. Through showing an advantage in a comparative analysis with control group taught by humans, we verified that the proposed system can contribute to evoke positive response of children with ASD and provide labor-saving effect on the clinical environment.

This paper presents the integration of audio-visual perception components for human robot interaction in the Robot Operating System (ROS). Visual-based nodes consist of skeleton tracking and gesture recognition using a depth camera, and face recognition using an RGB camera. Auditory perception is based on sound source localization using a microphone array. We present an integration framework of these nodes using a top-down hierarchical messaging protocol. On the top of the integration, a message carries information about the number of persons and their corresponding states (who, what, where), which are updated from many low-level perception nodes. The top message is passed to a planning node to make a reaction of the robot, according to the perception about surrounding people. This paper demonstrates human-robot interaction in multi-persons scenario where robot pays its attention to the speaking or waving hand persons. Moreover, this modularization architecture enables reusing modules for other applications. To validate this approach, two sound source localization algorithms are evaluated in real-time where ground-truth localization is provided by the face recognition module.

In this paper, we propose with a robot-assisted behavioral intervention system to easily improve children's social capability. In particular, the system for children with autism spectrum disorders (ASD) is basically achieved through the discrete trial teaching (DTT) protocol with three task modes of therapy, encouragement, and pause in social training scenarios. In child-robot interaction architecture, the robot firstly offers therapeutic training elements of mutual greeting and interplay game, and evaluates the level of children's reactivity by recognition modules for frontal face and touch features. Thence, the system in the decision-making process determines the task mode to perform subsequent action by grasping behavioral state of the children, and then it copes with individual response appropriately by using the robotic stimuli with the combination of kinesic acts and displayable contents. From the experiments of clinical trials with children with non-ASD and ASD in each robotic stimulus, the system showed the potential to increase their attention and activeness for social training, and we believe that the proposed system has some positive effect on developing children's social skills.