Abstract
Robust locomotion in a wide range of environments is still beyond the capabilities of robots. In this article, we explore how exploiting the soft morphology can be used to achieve stability in the commonly used spring-loaded inverted pendulum model. We evolve adaption rules that dictate how the attack angle and stiffness of the model should be changed to achieve stability for both offline and online learning over a range of starting conditions. The best evolved rules, for both the offline and online learning, are able to find stability from a significantly wider range of starting conditions when compared to an un-adapting model. This is achieved through the interplay between adapting both the control and the soft morphological parameters. We also show how when using the optimal online rule set, the spring-loaded inverted pendulum model is able to robustly withstand changes in ground level of up to 10 m downwards step size.
Highlights
Despite the increasing success of robots, autonomous locomotion in rough terrain still remains a challenge
We showed that it is possible to evolve optimal update rules for both offline and online learning for the spring-loaded inverted pendulum (SLIP) model
The attack angle increases with an increase in distance between episodes
Summary
Despite the increasing success of robots, autonomous locomotion in rough terrain still remains a challenge. In our previous work,[29] we systematically tested over 3200 different adaption strategies based on an offline learning approach that introduces changes of either the stiffness or the attack angle. If the model was not stable, it should follow a particular adaption strategy to change its parameter for the episode (i.e. stiffness or attack angle) This continued until the system was stable, or a set number of episodes was expired. In this article, we expand this previous work using evolutionary algorithms to explore a wider range of rule sets that allow adaption of both stiffness and attack angle. Using this new approach, we are able to exploit the tight interplay between the control parameter and soft morphology of the body. In the discussion section, we compare and contrast both learning methods
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