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2014
Zaraki, A.; Giuliani, M.; Dehkordi, M. B.; Mazzei, D.; D’ursi, A.; De Rossi, D. (2014) : An RGB-D based social behavior interpretation system for a humanoid social robot: 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM): Tehran, Iran: IEEE, S. 185-190

DOI: https://doi.org/10.1109/ICRoM.2014.6990898

Abstract: Humanoid social robots that interact with people need to be capable of interpreting the social behavior of their interaction partners in order to respond in a socially appropriate way. In this paper, we present a social behavior interpretation system that enables a humanoid robot to recognize human social behavior by analyzing communicative signals. The system receives the constructed RGB-D scene from a Kinect sensor, extracts information about body gesture and head pose from the scene using Microsoft Kinect SDK, and recognizes eight human social behaviors using a Hidden Markov Model (HMM). We trained the eight-state HMM with a corpus of 35 recorded human-human interaction scenes. The evaluation of the system shows a weighted average recognition rate of 81% for all states.

Keywords: Accuracy, Angemessen(heit) (von Technik), body gesture, eight-state HMM, Feature extraction, Gesture recognition, head pose, Hidden Markov model, Hidden Markov models, human social behavior, human-human interaction scenes, humanlike robot, humanoid social robot, human-robot interaction, ieee xplore, image colour analysis, image sensors, Joints, Kinect sensor, Microsoft Kinect SDK, pose estimation, RGB-D based social behavior interpretation system, RGB-D scene, Robot sensing systems, robot vision, social behavior interpretation system, social behavior recognition, Vectors, weighted average recognition rate
2013
Glas, D. F.; Wada, K.; Shiomi, M.; Kanda, T.; Ishiguro, H.; Hagita, N. (2013) : Personal service: A robot that greets people individually based on observed behavior patterns: 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI): Tokyo, Japan: IEEE, S. 129-130

DOI: https://doi.org/10.1109/HRI.2013.6483535

Abstract: We are developing an interactive service robot which provides personal greetings to customers, using a machine-learning approach based on observations of a customer’s appearance or behavior from on-board or environmental sensors. For each visit, several features are recorded, such as “time of day” or “number of people in group.” A set of classifiers trained by human coders compare the current features with the person’s individual history, to determine an appropriate feature for a robot to speak about. This system enables the robot to make context-appropriate comments such as “good morning, you’re here very early today.” We present the design of our system and an encouraging set of preliminary prediction results based on one month of data taken from real customers at a shopping mall.

Keywords: Accuracy, Angemessen(heit) (von Technik), context-appropriate comments, customer appearance, customer behavior, environmental sensors, Feature extraction, History, human coders, human-robot interaction, ieee xplore, interactive service robot, learning (artificial intelligence), long-term interaction, machine-learning approach, observed behavior patterns, personal greetings, personal service, Robot sensing systems, Sensors, service robot, shopping mall

2014

2013

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