Learning to Interpret Pointing Gestures: Experiments with Four-Legged Autonomous Robots

Hafner, Verena V.; Kaplan, Frédé́ric

doi:10.1007/11521082_13

Cited by 19 publications

(11 citation statements)

References 15 publications

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“…Joint attention with robots has been investigated, e.g., by Hafner and Kaplan [5]. They used simple edge-based features to recognize pointing gestures of Aibo dogs in a specific setting.…”

Section: Related Workmentioning

confidence: 99%

Towards joint attention for a domestic service robot - person awareness and gesture recognition using Time-of-Flight cameras

Droeschel

Stückler

Holz

et al. 2011

2011 IEEE International Conference on Robotics and Automation

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Abstract-Joint attention between a human user and a robot is essential for effective human-robot interaction. In this work, we propose an approach to person awareness and to the perception of showing and pointing gestures for a domestic service robot. In contrast to previous work, we do not require the person to be at a predefined position, but instead actively approach and orient towards the communication partner. For perceiving showing and pointing gestures and for estimating the pointing direction a Time-of-Flight camera is used. Estimated pointing directions and shown objects are matched to objects in the robot's environment.Both the perception of showing and pointing gestures as well as the accurary of estimated pointing directions have been evaluated in a set of different experiments. The results show that both gestures are adequatly perceived by the robot. Furthermore, our system achieves a higher accuracy in estimating the pointing direction than is reported in the literature for a stereo-based system. In addition, the overall system has been successfully tested in two international RoboCup@Home competitions and the 2010 ICRA Mobile Manipulation Challenge.

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“…Joint attention with robots has been investigated, e.g., by Hafner and Kaplan [5]. They used simple edge-based features to recognize pointing gestures of Aibo dogs in a specific setting.…”

Section: Related Workmentioning

confidence: 99%

Towards joint attention for a domestic service robot - person awareness and gesture recognition using Time-of-Flight cameras

Droeschel

Stückler

Holz

et al. 2011

2011 IEEE International Conference on Robotics and Automation

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“…Several of these projects have involved machine learning. Examples are robotic clicker training [56], experiments on neural learning for pointing gestures between two robots [43], or studies in curiosity-driven developmental robotics [55], [86], [87]. Researchers at Sony implemented AIBO's behaviour control architecture using probabilistic state machines whose probabilities are modified using reinforcement learning through interaction with the user [34].…”

Section: Machine Learning On Aibos In Generalmentioning

confidence: 99%

Machine Learning With AIBO Robots in the Four-Legged League of RoboCup

Chalup

Murch

Quinlan

2007

IEEE Trans. Syst., Man, Cybern. C

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Abstract-Robot learning is a growing area of research at the intersection of robotics and machine learning. The main contributions of this article include a review of how machine learning has been used on Sony AIBO robots and at RoboCup with a focus on the four-legged league during the years 1998-2004. The review shows that the application oriented use of machine learning in the four-legged league was still conservative and restricted to a few well-known and easy to use methods such as standard decision trees, evolutionary hill climbing, and support vector machines. Method oriented spin-off studies emerged more frequently and increasingly addressed new and advanced machine learning techniques. Further the article presents some details about the growing impact of machine learning in the software system developed by authors' robot soccer team-the NUbots.

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“…Marjanovic et al [5] introduced a motor-vision mapping system that learns to perform pointing motions towards visual targets, Marjanovic's system has an explicit goal of pointing, this behavior does not emerge as a side effect of other developmental processes occurring at the same time. Hafner and Kaplan [6] applied multi-layer-perceptron network to train a robot to interpret pointing gestures of another robot, the robot's function is limited, because they used a fixed topological structure network to implement their learning system. Shademan et al [7] applied a locally least squares based Jacobian estimation method to build a robotic visual-motor learning system, which is lack of developmental perspective, merely a matrix calculation.…”

Section: Introductionmentioning

confidence: 99%

Integration of Brain-like neural network and infancy behaviors for robotic pointing

Wang

Chao

2014

2014 International Conference on Information Science, Electronics and Electrical Engineering

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This paper introduces a new approach to learning pointing behavior in a developmental robot by using a type of constructive neural network and Q-learning algorithm, taking inspirations from human infant development. The pointing behavior is considered as the first movement that human infants use to communicate with other person during human development, it is also the foundation of the human social interaction abilities. We rebuilt this developmental course in our robot simulation system. The learning algorithm of the pointing is implemented by Q-Learning, and a radial based function neural network with resource allocating algorithm is applied to hold the learning result and to control robot movements. The experimental results show that the approach is able to lead our development robot to generate pointing behavior.

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Learning to Interpret Pointing Gestures: Experiments with Four-Legged Autonomous Robots

Cited by 19 publications

References 15 publications

Towards joint attention for a domestic service robot - person awareness and gesture recognition using Time-of-Flight cameras

Towards joint attention for a domestic service robot - person awareness and gesture recognition using Time-of-Flight cameras

Machine Learning With AIBO Robots in the Four-Legged League of RoboCup

Integration of Brain-like neural network and infancy behaviors for robotic pointing

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