Mechanical cognitivization: a kinematic system proof of concept

Abstract

The common approach for training robots is to expose them to different environmental scenarios, training their controllers to have the best possible commands when untrained scenarios are encountered. When humans train, they do the same. They try new manipulations by performing within different environments. However, humans training also includes a type of training which, although claimed to improve cognitive capabilities, has not, to date, been adopted for the training of robots. This type of training involves the restriction of manipulation capabilities while performing different tasks, e.g. climbing with just one hand. Recently research that aims at exploring the invigorating idea that such training, termed as mechanical cognitivization (MC), would enhance the robustness of robots, has been published. However, that work has demonstrated the idea by utilizing mathematical functions. In the current paper, the first proof that such training may enhance the robustness of robots is given by using a kinematic system. Specifically, it is shown that such training improves the performances of robots when encountering the need to perform new tasks, when performing within untrained environments and when malfunctions occur. The advantages of the suggested training are highlighted through facilitating a comparison between three learning schemes that include a common neural net and two MC-based nets. These are termed here as amalgamated modes neural net and committee of modes neural net. It is shown that both MC-based schemes are superior when robustness is considered.