Name
Abstract | ||
|---|---|---|
The formulation and optimization of joint trajectories for humanoid robots is quite different from this same task for standard robots because of the complexity of humanoid robots’ kinematics and dynamics. In this paper we exploit the similarity between human motion and humanoid robot motion to generate joint trajectories for humanoids. In particular, we show how to transform human motion information captured by an optical tracking device into a high dimensional trajectory for a humanoid robot. We propose an automatic approach to relate humanoid robot kinematic parameters to the kinematic parameters of a human performer. Based on this relationship we infer the desired trajectories in robot joint space. B-spline wavelets are utilized to efficiently represent the trajectories. The density of the basis functions on the time axis is selected automatically. Large-scale optimization techniques are employed to solve the underlying computational problems efficiently. We applied our method to the task of teaching a humanoid robot how to make various naturally looking movements. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/j.robot.2004.03.004 | Robotics and Autonomous Systems |
Keywords | Field | DocType |
Humanoid robots,Joint trajectories,Optimization | Computer vision,Robot control,Computational problem,Kinematics,Simulation,Computer science,Robot kinematics,Artificial intelligence,Basis function,Robot,Trajectory,Humanoid robot | Journal |
Volume | Issue | ISSN |
47 | 2 | 0921-8890 |
Citations | PageRank | References |
76 | 4.88 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ales Ude | 1 | 898 | 85.11 |
| Christopher G. Atkeson | 2 | 5441 | 849.86 |
| Marcia Riley | 3 | 374 | 37.87 |