Scaffolded Learning of In-place Trotting Gait for a Quadruped Robot with Bayesian Optimization
This addresses safety and efficiency in gait learning for quadruped robots, though it is incremental as it builds on existing methods like Bayesian Optimization and Raibert controllers.
The paper tackled the problem of safely learning a stable trotting gait for a quadruped robot by using instructional scaffolding to reduce failure risks during trials, and found that gradually reduced support led to a more stable gait compared to fixed support.
During learning trials, systems are exposed to different failure conditions which may break robotic parts before a safe behavior is discovered. Humans contour this problem by grounding their learning to a safer structure/control first and gradually increasing its difficulty. This paper presents the impact of a similar supports in the learning of a stable gait on a quadruped robot. Based on the psychological theory of instructional scaffolding, we provide different support settings to our robot, evaluated with strain gauges, and use Bayesian Optimization to conduct a parametric search towards a stable Raibert controller. We perform several experiments to measure the relation between constant supports and gradually reduced supports during gait learning, and our results show that a gradually reduced support is capable of creating a more stable gait than a support at a fixed height. Although gaps between simulation and reality can lead robots to catastrophic failures, our proposed method combines speed and safety when learning a new behavior.