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ROBOT BEHAVIOUR CONTROL

Student E.E. Shelomentsev Group 8 Е 00 Scientific supervisor Т .V. Alexandrova Language supervisor T.I.Butakova.

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ROBOT BEHAVIOUR CONTROL

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  1. Student E.E. Shelomentsev Group 8Е00 Scientific supervisor Т.V. Alexandrova Language supervisor T.I.Butakova ROBOT BEHAVIOUR CONTROL SUCCESSFUL TRIAL OF MARKERLESS MOTION CAPTURE TECHNOLOGY

  2. Plan • Introduction • Methodology • Markerless Motion Capture • HAMMER architecture • Results • Conclusion

  3. Current State of Robotics Industrial robotics Social robotics

  4. What will we do? • The main goals of our research: • - to develop and try a new method of human motions recognizing • - to create software for the robot which will build an appropriate model of the robot’s behavior with using the new method of human motions recognizing

  5. Motion Capture Marker Technology Mechanical Technology

  6. Markerless Motion Capture Human RGB-D Sensor Obtained Data

  7. Hierarchical Attentive Multiple Models for Execution and Recognition (HAMMER) Purposes of use: • To determine the intentions of the human • To form the robot reactions to various actions

  8. HAMMER architecture

  9. Results

  10. Conclusion What have we done?

  11. References • S. Schaal, The New Robotics-towards human-centered machines, HFSP journal, vol. 1, no. 2, pp. 115–26, 2007. • Y. Demiris, Prediction of intent in robotics and multi-agent systems, Cognitive processing, vol. 8, no. 3, pp. 151–158, 2007. • http://en.wikipedia.org/wiki/Motion_captue • Arnaud Ramey, Víctor González-Pacheco, Miguel A Salichs. Integration of a Low-Cost RGB-D Sensor in a Social Robot for Gesture Recognition. 6th international conference on Humanrobot interaction HRI 11, 2011 • Miguel Sarabia, Raquel Ros, YiannisDemiris. Towards an open-source social middleware for humanoid robots, 11th IEEE-RAS International Conference on Humanoid Robots, 2011 • Y. Demiris and B. Khadhouri, Hierarchical Attentive Multiple Models for Execution and Recognition (HAMMER), Robotics and Autonomous Systems, vol. 54, no. 5, pp. 361–369,2006 • Abstraction in Recognition to Solve the Correspondence Problem for Robot Imitation, in Proc. of the Conf. Towards Autonomous Robotics Systems, 2004, pp. 63–70. • M. F. Martins and Y. Demiris, Learning multirobot joint action plans from simultaneous task execution demonstrations, in Proc. of the Intl. Conf. on Autonomous Agents and Multiagent Systems, vol. 1, 2010, pp. 931–938. • S. Butler and Y. Demiris, Partial Observability During Predictions of the Opponent’s Movements in an RTS Game, in Proc. of the Conf. on Computational Intelligence and Games, 2010, pp. 46–53. • A. Karniel, Three creatures named ‘forward model’, Neural Networks, vol. 15, no. 3, pp. 305–7, 2002. • Y. Wu, Y. Demiris, Learning Dynamical Representations of Tools for Tool-Use Recognition, IEEE International Conference on Robotics and Biomimetics, 2011

  12. Mission Completed! Next research can be found here: see4me@mail.ru Student E.E. Shelomentsev Group 8Е00 Scientific supervisor Т.V. Alexandrova Language supervisor T.I.Butakova ROBOT BEHAVIOUR CONTROL SUCCESSFUL TRIAL OF MARKERLESS MOTION CAPTURE TECHNOLOGY

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