Robust Learning of Embodied Models under Resource Constraints
DOI:
https://doi.org/10.66238/fsrma61Keywords:
Embodied Artificial Intelligence, Robust Reinforcement Learning, Resource Constraints, Edge ComputingAbstract
The deployment of embodied artificial intelligence in real world environments presents a profound challenge at the intersection of robust machine learning and resource constrained systems. Embodied models, such as autonomous navigating robots or robotic manipulators, must process high dimensional multimodal sensory streams in real time to produce physical actions. However, these systems frequently operate under strict limitations regarding computational capacity, memory bandwidth, and energy consumption. Furthermore, the physical world is inherently stochastic, necessitating high algorithmic robustness to domain shifts and sensor noise. This paper presents a comprehensive algorithmic framework for robust learning in embodied models subjected to extreme resource constraints. We propose a novel resource aware reinforcement learning paradigm that dynamically balances policy robustness with computational expenditure. Through extensive empirical analysis across multiple simulated physical environments, we demonstrate that our approach maintains baseline performance while drastically reducing floating point operations and memory footprint. We evaluate the proposed methodology against state of the art baselines in both rigid body physics simulators and photorealistic navigation environments. Our results indicate that dynamic compute allocation combined with robust policy distillation enables stable policy deployment on edge hardware without catastrophic failure during out of distribution domain shifts. The findings provide a foundational pathway for scaling embodied intelligence on practical robotic platforms.
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