Online transfer of human motion to humanoids

International Journal of Humanoid Robotics

Volumn 6, Issue 2, 2009, Pages 265-289

  Dariush, Behzad ^a   Gienger, Michael ^b   Arumvbakkam, Arjun ^a   Zhu, Youding ^c   Jian, Bing ^d   Fujimura, Kikuo ^a   Goerick, Christian ^b  

^a HONDA RESEARCH INSTITUTE   (United States)

^b HONDA RESEARCH INSTITUTE EUROPE GMBH   (Germany)

^c Ohio State University   (United States)

^d University of Florida   (United States)

Author keywords

Imitation learning; Retargeting; Task space control

Indexed keywords

ANATOMICAL LANDMARKS; DEPTH IMAGE; HUMAN MOTION DATA; HUMAN MOTIONS; HUMAN ROBOT INTERACTIONS; HUMANOID ROBOT; IMITATION LEARNING; JOINT LIMITS; JOINT VELOCITY; LOW DIMENSIONAL; MOTION CAPTURE SYSTEM; MOTION RETARGETING; MOTION TRANSFER; OPTIMIZATION APPROACH; POINT DETECTION; RETARGETING; ROBOT JOINT; TASK SPACE CONTROL; TIME-OF-FLIGHT IMAGING; VISION ALGORITHMS; VISION BASED;

INTELLIGENT ROBOTS;

EID: 68049083505     PISSN: 02198436     EISSN: None     Source Type: Journal    
DOI: 10.1142/S021984360900170X     Document Type: Article

References (31)

1. Swiss Ranger SR-3000 3D Time of Flight Camera. http://http:// www.swissranger.ch/.
2. H. Bekkering, A. Wohlschlaeger and M. Gattis, Imitation of gestures in children is goal directed, Quarterly Journal of Experimental Psychology 53A(1) (2000) 153-164.
3. N. Bernstein, The Coordination and Regulation of Movements. Pergamon, London (1967).
4. B. Bolder, M. Dunn, M. Gienger, H. Janssen, H. Sugiura and C. Goerick, Visually guided whole body interaction, Proceedings of the Int. Conf. on Robotics and Automation, Rome, Italy (2007).
5. Carnegie Mellon University, CMU Graphics Lab Motion Capture Database Internet page. http://mocap.cs.cmu.edu/.
6. T. F. Chan and R. V. Dubey, A weighted least-norm solution based scheme for avoiding joint limits for redundant joint manipulators, IEEE Transactions on Robotics and Automation 11(2) (1995).
7. K. J. Choi and H. S. Ko, On-line motion retargeting, Journal of Visualization and Computer Animation 11(5) (2000) 223-235.
8. M. Gienger, H. Janssen and C. Goerick, Task-oriented whole body motion for humanoid robots, Proceedings of the 2005 5th IEEE-RAS Int. Conf. on Humanoid Robots, Los Angeles, USA (2005), pp. 238-244.
9. M. Gleicher, Retargeting motion to new characters, Proceedings of SIGGRAPH98, ACM, New York (1998), pp. 33-42.
10. F. Guenter and A. G. Billard, Using reinforcement learning to adapt an imitation task. Conf. on Intelligent Robots and Systems (IROS07), San Diego, CA (2007), pp. 1022-1027.
11. M. Hirose, Y. Haikawa, T. Takenaka and K. Hirai, Development of humanoid robot ASIMO, IEEE/RSJ Int. Conf. on Intelligent Robots and Systems - Workshop 2, Hawaii, USA (2001).
12. Honda Motor Corp. The Honda Humanoid Robot Asimo. Internet page, http:// www.world.honda.com/ASIMO.
13. S. Kagami, F. Kanehiro, Y. Tamiya, M. Inaba and H. Inoue, Autobalancer: An online dynamic balance compensation scheme for humanoid robots, Int. Workshop Alg. Found. Robot.(WAFR), Lausanne, Switzerland (2000).
14. J. Lee and S. Y. Shin, A hierarchical approach to interactive motion editing for human-like figures, Proceedings of SIGGRAPH99, ACM, New York (1999), pp. 39-48.
15. J. Luh, M. Walker and R. Paul, Resolved-acceleration control of mechanical manipulators, IEEE Transactions on Automatic Control 25 (1980) 468-474.
16. Y. Nakamura, Advanced Robotics, Redundancy and Optimization. Adisson-Wesley (1991).
17. Y. Nakamura and H. Hanafusa, Inverse kinematic solution with singularity robustness for robot manipulator control, ASME J. Dyn. Sys. Meas., Contr. 108(3) (1986) 163-171.
18. S. Nakaoka, A. Nakazawa, F. Kanehiro, K. Kaneko, M. Morisawa, H. Hirukawa and K. Ikeuchi, Learning from observation paradigm: Leg task models for enabling a biped humanoid robot to imitate human dances. Int. J. Robotics Research (2007), pp. 829-844.
19. A. Nakazawa, S. Nakaoka, K. Ikeuchi and K. Yokoi, Imitating human dance motions through motion structure analysis, Int. Conf. on Intelligent Robots and Systems (IROS), Lausanne, Switzerland (2002), pp. 2539-2544.
20. N. Pollard, J. K. Hodgins, M. Riley and C. Atkeson, Adapting human motion for the control of a humanoid robot, Proceedings of the IEEE Int. Conf. on Robotics and Automation (ICRA '02) (2002).
21. B. Robins, K. Dautenhahn, R. Boekhorst and A. Billard, Robotic assistants in therapy and education of children with autism: Can a small humanoid robot encourage social interaction skills? Universal Access in the Information Society (UAIS) 4(2) (2005) 105-120.
22. A. Safonova, N. Pollard and J. Hodgins, Optimizing human motion for the control of a humanoid robot, Int. Symp. on Adaptive Motion of Animals and Machines (AMAAM2003), Kyoto, Japan (2003).
23. S. Schaal, Learning from demonstration. In M. Mozer, M. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems Chapter 9, MIT Press (1997), pp. 1040-1046.
24. L. Sentis and O. Khatib, A whole-body control framework for humanoids operating in human environments, Proc. of Int. Conf. on Robotics and Automation (ICRA06), Orlando, FL (2006).
25. B. Siciliano and J. Slotine, A general framework for managing multiple tasks in highly redundantrobotic systems, Int. Conf. on Advanced Robotics, Pisa, Italy 2 (1991) 1211-1216.
26. H. Sugiura, M. Gienger, H. Janssen and C. Goerick, Real-time collision avoidance with whole body motion control for humanoid robots, IEEE Int. Conf. on Intelligent Robots and Systems (IROS 2007).
27. S. Tak and H. Ko, A physically-based motion retargeting filter, ACM Trans. on Graphics 24(1) (2005) 98-117.
28. S. Tak, O. Song and H. Ko, Motion balance filtering, Comput. Graph. Forum. (Eurograhics 2000) 19(3) (2000) 437-446.
29. A. Ude, C. Atkeson and M. Riley, Programming full-body movements for humanoid robots by observation, Robotics and Autonomous Systems 47 (2004) 93-108.
30. UNC Chapell Hill, Swift++ Library. Speedy walking via improved feature testing for non-convex objects. Internet page. http://www.cs.unc.edu/ ~geom/SWIFT++/.
31. Y. Zhu, B. Dariush and K. Fujimura, Controlled human pose estimation from depth image streams, CVPR Workshop on Time of Flight Computer Vision, Anchorage, Alaska (2008).