The availability of inexpensive 3D-printed quadrupedal robots motivates the development of learning-based methods compatible with low-cost embedded processors and position-controlled hobby servos. In this work, we show that a linear policy is sufficient to modulate an open-loop trajectory generator, enabling a quadruped to walk over rough, unknown terrain, with limited sensing. The policy is trained in simulation using randomized terrain and dynamics and directly deployed on the robot. We show that the resulting controller can be implemented on resource-constrained systems. We demonstrate the results by deploying the policy on the OpenQuadruped, an open-source 3D-printed robot equipped with hobby servos and an embedded microprocessor.
|Title of host publication
|IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 2021
|2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021 - Prague, Czech Republic
Duration: Sep 27 2021 → Oct 1 2021
|IEEE International Conference on Intelligent Robots and Systems
|2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
|9/27/21 → 10/1/21
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Vision and Pattern Recognition
- Computer Science Applications