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Embodiment and Its Influence on Informational Costs of Decision Density-Atomic Actions vs. Scripted Sequences
Bente Riegler1, Daniel Polani1, Volker Steuber2
1Sepia Lab, Adaptive Systems Group, School of Engineering and Computer Science, University of Hertfordshire, Hatfield, United Kingdom.
Frontiers in Robotics and AI
|May 17, 2021
Summary
Embodiment significantly impacts robot decision-making by influencing information costs. Well-labeled ("embodied") robots, especially when using scripts, incur lower costs than "twisted" or unlabeled ones, improving performance.
Area of Science:
- Robotics
- Artificial Intelligence
- Cognitive Science
Background:
- The significance of embodiment in robot performance has long been theorized.
- Recent advancements include quantitative models to assess embodiment benefits.
- This study applies a relevant information model to analyze embodiment's effect on decision density.
Purpose of the Study:
- To quantitatively model how embodiment influences decision density using relevant information.
- To investigate the impact of action scripts on information costs.
- To compare decision costs in well-labeled versus mislabeled ('twisted') environments.
Main Methods:
- Utilizing a quantitative model based on relevant information.
- Analyzing a minimalistic navigation task with atomic actions and action scripts.
- Comparing information costs in 'embodied' (well-labeled) and 'twisted' (mislabeled) worlds.
Main Results:
- 'Twisted' worlds incur significantly higher relevant information costs than 'embodied' worlds.
- Employing scripts increases information costs, particularly in 'twisted' scenarios, by reducing decision density.
- The study quantifies the objective benefits of well-embodied agents.
Conclusions:
- Well-labeled embodiments demonstrably reduce information costs in robotic decision-making.
- Action scripts can exacerbate decision costs in poorly labeled environments.
- This research provides a quantifiable basis for preferring well-embodied robotic systems.
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