Name
Abstract | ||
|---|---|---|
In this letter, we propose a real-time self-collision avoidance approach for whole-body humanoid robot control. To achieve this, we learn the feasible regions of control in the humanoid's joint space as smooth self-collision boundary functions. Collision-free motions are generated online by treating the learned boundary functions as constraints in a Quadratic Program based Inverse Kinematic solver. As the geometrical complexity of a humanoid robot joint space grows with the number of degrees-of-freedom (DoF), learning computationally efficient and accurate boundary functions is challenging. We address this by partitioning the robot model into multiple lower-dimensional submodels. We compare performance of several state-of-the-art machine learning techniques to learn such boundary functions. Our approach is validated on the 29-DoF iCub humanoid robot, demonstrating highly accurate real-time self-collision avoidance. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LRA.2021.3057024 | IEEE ROBOTICS AND AUTOMATION LETTERS |
Keywords | DocType | Volume |
Robots, Humanoid robots, Manipulators, Legged locomotion, Collision avoidance, Support vector machines, Torso, Collision avoidance, humanoid robot systems, machine learning for robot control | Journal | 6 |
Issue | ISSN | Citations |
2 | 2377-3766 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mikhail Koptev | 1 | 0 | 0.34 |
| Nadia Figueroa | 2 | 48 | 8.64 |
| Aude Billard | 3 | 7 | 3.86 |