Book contents
- Frontmatter
- Contents
- Contributors
- Acknowledgments
- Part I Introduction
- Part II Taxonomy of Metacognitive Approaches
- Part III Neuro-Symbolic Models in AI
- Part IV Metacognition with LLMS
- Part V Metacognition in Learning Agents
- 9 Uncertainty Quantification’s Role in Metacognition
- 10 The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning
- Part VI Assured Machine Learning in High-Stakes Domains
- Part VII Metacognition as a Solution to Handle Failure
- Part VIII Applications of Metacognitive AI
10 - The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning
from Part V - Metacognition in Learning Agents
Published online by Cambridge University Press: 08 September 2025
Book contents
- Frontmatter
- Contents
- Contributors
- Acknowledgments
- Part I Introduction
- Part II Taxonomy of Metacognitive Approaches
- Part III Neuro-Symbolic Models in AI
- Part IV Metacognition with LLMS
- Part V Metacognition in Learning Agents
- 9 Uncertainty Quantification’s Role in Metacognition
- 10 The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning
- Part VI Assured Machine Learning in High-Stakes Domains
- Part VII Metacognition as a Solution to Handle Failure
- Part VIII Applications of Metacognitive AI
Summary
The chapter discusses the critical role of predictive uncertainty and diversity in enhancing the robustness and generalizability of embodied AI and robot learning. It explores the need for robots to efficiently learn and act in the unpredictable physical world by considering diverse scenarios and their consequences. The chapter highlights the importance of distinguishing between evaluative and generative paradigms of uncertainty, emphasizing the need to balance accuracy, uncertainty, and computational complexity in robot models. It examines various sources of uncertainty, including physical and model limitations, partial observability, environment dynamics, and domain shifts. Additionally, it outlines techniques for quantifying uncertainty, such as variance, entropy, and Bayesian methods, and underscores the significance of leveraging uncertainty in decision-making, exploration, and learning robust models. By addressing uncertainty in perception, representation, planning, and control, the chapter aims to improve the reliability and safety of robotic systems in diverse and dynamic environments.
Information
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- Chapter
- Information
- Metacognitive Artificial Intelligence , pp. 148 - 182Publisher: Cambridge University PressPrint publication year: 2025
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