Exploring the Effect of Dynamic Drive Balancing in Open-ended Learning Robots

Abstract

This paper seeks to explore the effect and possibilities of autonomously balancing drives in a motivational architecture aimed at open-ended learning robots. These types of robots are very useful in unconstrained human robot interaction settings or when uncontrolled dynamic scenarios that are unknown at design time must be addressed. Designing a robot under these conditions implies that it must be endowed with some primary operational purpose and some additional self-preservation objectives whose fulfillment depend on the characteristics of the particular domain it is facing each moment in time. Domains that are not known beforehand and for which no a priori goal or skill structure can be designed in. Thus, an approach to the design and engineering of motivational structures to endow robots with specific purposes is proposed and tested here. We concentrate on the drive structure of a motivational system and the effects of its autonomous adaptation to changing circumstances. To provide for this adaptation, a simple evolutionary strategy is defined for the autonomous regulation of multiple drives seeking to optimize long-term operation. The proposal is tested on a Baxter robot performing an industrial task and the results confirm the potential of autonomous dynamic drive balancing as a tool in open-ended settings.