Transcript portal.tpu.ru:7777

Student
Group
Scientific supervisor
Language supervisor
E.E. Shelomentsev
8Е00
Т.V. Alexandrova
T.I.Butakova
ROBOT BEHAVIOUR CONTROL
SUCCESSFUL TRIAL OF MARKERLESS MOTION CAPTURE TECHNOLOGY
Plan
• Introduction
• Methodology
• Markerless Motion Capture
• HAMMER architecture
• Results
• Conclusion
Current State of Robotics
Industrial robotics
Social robotics
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
Motion Capture
Marker Technology
Mechanical Technology
Markerless Motion Capture
Human
RGB-D Sensor
Obtained Data
Hierarchical Attentive Multiple Models for Execution
and Recognition (HAMMER)
Purposes of use:
World State
• To determine the intentions of the
human
Inverse
Models
• To form the robot reactions to various
actions
HAMMER
Confidence
Evaluation
Function
Forward
Models
Action
Signals
HAMMER architecture
Results
Robot simulates the motions of the
operator
Robot teaches children to dance
Conclusion
Robot Reflex
System
Problem of motion
recognizing
Application of the
Markerless Motion
Capture
technology
What have we done?
Problem of robot
reactions building
Implementation of
the HAMMER
algorithm
References
1. S. Schaal, The New Robotics-towards human-centered machines, HFSP journal, vol. 1, no. 2, pp. 115–26, 2007.
2. Y. Demiris, Prediction of intent in robotics and multi-agent systems, Cognitive processing, vol. 8, no. 3, pp. 151–158,
2007.
3. http://en.wikipedia.org/wiki/Motion_captue
4. 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
5. Miguel Sarabia, Raquel Ros, Yiannis Demiris. Towards an open-source social middleware for humanoid robots, 11th
IEEE-RAS International Conference on Humanoid Robots, 2011
6. 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
7. Abstraction in Recognition to Solve the Correspondence Problem for Robot Imitation, in Proc. of the Conf. Towards
Autonomous Robotics Systems, 2004, pp. 63–70.
8. 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.
9. 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.
10. A. Karniel, Three creatures named ‘forward model’, Neural Networks, vol. 15, no. 3, pp. 305–7, 2002.
11. Y. Wu, Y. Demiris, Learning Dynamical Representations of Tools for Tool-Use Recognition, IEEE International
Conference on Robotics and Biomimetics, 2011
Mission Completed!
Next research can be found here:
[email protected]
Student
Group
Scientific supervisor
Language supervisor
E.E. Shelomentsev
8Е00
Т.V. Alexandrova
T.I.Butakova
ROBOT BEHAVIOUR CONTROL
SUCCESSFUL TRIAL OF MARKERLESS MOTION CAPTURE TECHNOLOGY