Imitation of Walking Paths with a Low-Cost Humanoid Robot

The ability to follow specific and task-relevant paths is an essential feature for legged humanoid robots. In this paper, we describe a technique for programming walking paths for humanoid robots based on imitation. Demonstrated paths are synthesized as NURBS (Non Uniform Rational B-Spline), and can be adapted by the robot based on local and dynamic information. We report simple experiments performed with a Robosapien V2 low-cost humanoid toy which has been suitably enhanced to support imitation-based programming. We show that despite robot limitations rather complex navigational tasks can be achieved through visual guidance. The system combines inputs from multiple sensory devices, including a motion tracker and a monocular vision system for landmark recognition.

[1]  K. Dautenhahn,et al.  Imitation in Animals and Artifacts , 2002 .

[2]  Yangsheng Xu,et al.  Learning human navigational skill for smart wheelchair , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Stefan Schaal,et al.  Is imitation learning the route to humanoid robots? , 1999, Trends in Cognitive Sciences.

[4]  Giovanni Flammia.  What's Next for the E-Book? , 2000, IEEE Intell. Syst..

[5]  Dong-Soo Kwon,et al.  Mobile robots at your fingertip: Bezier curve on-line trajectory generation for supervisory control , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[6]  Katsushi Ikeuchi,et al.  Toward an assembly plan from observation. I. Task recognition with polyhedral objects , 1994, IEEE Trans. Robotics Autom..

[7]  Pradeep K. Khosla,et al.  Learning by observation with mobile robots: a computational approach , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[8]  Aude Billard,et al.  Robota: Clever toy and educational tool , 2003, Robotics Auton. Syst..

[9]  Hirokazu Kato,et al.  Marker tracking and HMD calibration for a video-based augmented reality conferencing system , 1999, Proceedings 2nd IEEE and ACM International Workshop on Augmented Reality (IWAR'99).

[10]  C. Breazeal,et al.  Challenges in building robots that imitate people , 2002 .

[11]  Masayuki Inaba,et al.  Vision-based 2.5D terrain modeling for humanoid locomotion , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[12]  Sven Behnke,et al.  Playing Soccer with RoboSapien , 2005, RoboCup.

[13]  Hideki Hashimoto,et al.  Human-following mobile robot in a distributed intelligent sensor network , 2004, IEEE Transactions on Industrial Electronics.

[14]  Takeo Kanade,et al.  Vision-guided humanoid footstep planning for dynamic environments , 2005, 5th IEEE-RAS International Conference on Humanoid Robots, 2005..

[15]  Maja J. Mataric,et al.  Getting Humanoids to Move and Imitate , 2000, IEEE Intell. Syst..

[16]  Takeo Kanade,et al.  Online environment reconstruction for biped navigation , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..