Abstract:
Tumbling locomotion allows for small robots to traverse comparatively rough terrain, however, their motion is complex and difficult to control. Existing tumbling robot co...View moreMetadata
Abstract:
Tumbling locomotion allows for small robots to traverse comparatively rough terrain, however, their motion is complex and difficult to control. Existing tumbling robot control methods involve manual control or the assumption of at terrain. Reinforcement learning allows for the exploration and exploitation of diverse environments. By utilizing reinforcement learning with domain randomization, a robust control policy can be learned in simulation then transferred to the real world. In this paper, we demonstrate autonomous setpoint navigation with a tumbling robot prototype on at and non- at terrain. The flexibility of this system improves the viability of nontraditional robots for navigational tasks.
Date of Conference: 24 October 2020 - 24 January 2021
Date Added to IEEE Xplore: 10 February 2021
ISBN Information: