Safe Exploration in Embodied Reinforcement Learning

Authors

  • Aaron Phillips Author

Keywords:

safe reinforcement learning, embodied AI, exploration, robotics, constraints

Abstract

This review examines safe exploration in embodied reinforcement learning. The organizing question is how agents can learn informative behaviour while respecting physical, social, and operational constraints. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to treating simulated constraint satisfaction as proof of physical-world safety. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in robotics and autonomous embodied systems.

References

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Published

2026-08-08

Issue

Vol. 1 (2026): Archive

Section

Review

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Copyright (c) 2026 Advances in Adaptive Intelligence

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This work is licensed under a Creative Commons Attribution 4.0 International License.