Embodiment and Its Influence on Informational Costs of Decision Density—Atomic Actions vs. Scripted Sequences

• Main Authors: Bente Riegler, Daniel Polani, Volker Steuber
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-04-01
• Series: Frontiers in Robotics and AI
• Subjects: agent control; information theory; embodiment; Reinforcement learning; morphological computing
• Online Access: https://www.frontiersin.org/articles/10.3389/frobt.2021.535158/full

The importance of embodiment for effective robot performance has been postulated for a long time. Despite this, only relatively recently concrete quantitative models were put forward to characterize the advantages provided by a well-chosen embodiment. We here use one of these models, based on the concept of relevant information, to identify in a minimalistic scenario how and when embodiment affects the decision density. Concretely, we study how embodiment affects information costs when, instead of atomic actions, scripts are introduced, that is, predefined action sequences. Their inclusion can be treated as a straightforward extension of the basic action space. We will demonstrate the effect on informational decision cost of utilizing scripts vs. basic actions using a simple navigation task. Importantly, we will also employ a world with “mislabeled” actions, which we will call a “twisted” world. This is a model which had been used in an earlier study of the influence of embodiment on decision costs. It will turn out that twisted scenarios, as opposed to well-labeled (“embodied”) ones, are significantly more costly in terms of relevant information. This cost is further worsened when the agent is forced to lower the decision density by employing scripts (once a script is triggered, no decisions are taken until the script has run to its end). This adds to our understanding why well-embodied (interpreted in our model as well-labeled) agents should be preferable, in a quantifiable, objective sense.