Abstract
Robot-to-robot learning, a specific case of social learning in robotics, enables multiple robots to share learned skills while completing a task. The literature offers various statements of its benefits. Robots using this type of social learning can reach a higher performance, an increased learning speed, or both, compared to robots using individual learning only. No general explanation has been advanced for the difference in observations, which make the results highly dependent on the particular system and parameter setting. In this paper, we perform a detailed analysis into the effects of robot-to-robot learning. As a result, we show that this type of social learning can reduce the sensitivity of the learning process to the choice of parameters in two ways. First, robot-to-robot learning can reduce the number of bad performing individuals in the population. Second, robot-to-robot learning can increase the chance of having a successful run, where success is defined as the presence of a high performing individual. Additionally, we show that robot-to-robot learning results in an increased learning speed for almost all parameter settings. Our results indicate that robot-to-robot learning is a powerful mechanism which leads to benefits in both performance and learning speed.
Highlights
The widely used definition of social learning reflects animal behavior: social learning is learning through observation of conspecifics
The benefits of robot-to-robot learning were measured in three ways: (1) the success rate, which is the percentage of runs that have a good controller in the final generation (2) the population failure, which is the median of the percentage of bad individuals in the final generation and (3) the learning speed, which is the numerical integral of the median performance over time
Existing literature in robotto-robot learning typically compares individual learning with robot-to-robot learning for only one parameter setting
Summary
The widely used definition of social learning reflects animal behavior: social learning is learning through observation of conspecifics. Considering humans the definition can be extended: social learning is learning through observation of conspecifics or transferring knowledge through language. The ability to use language offers a new method, a second tool in the toolbox of social learning. If robots are concerned social learning is learning through observation of conspecifics or transferring knowledge through language or direct exchange of (parts of) controllers. In the current paper we focus on the third option for robots, the direct exchange of controllers, that is a special case of social learning that is only available for robots. To emphasize this we usethe term robot-to-robot learning
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