Technology Category
Intelligent Robots: Behavior Learning & ControlHumans and AI: Human-Aware Planning and Behavior PredictionMachine Learning: Imitation Learning & Inverse Reinforcement Learning
Control-Optimized Deep Reinforcement Learning for Artificially Intelligent Autonomous Systems
🤖 AI Summary
Traditional deep reinforcement learning (DRL) assumes perfect action execution, neglecting system dynamics, hardware constraints, and actuation delays—leading to substantial performance degradation upon real-world deployment. To address this, we propose a control-aware DRL framework featuring a novel two-stage action mechanism: first generating a desired action, then producing a compensatory control signal via a learnable controller; execution errors and their dynamic compensation are explicitly modeled during training. Leveraging control-theoretic principles, we refactor five open-source robotic simulation environments to incorporate realistic uncertainties, enabling precise error modeling and online correction. Experiments demonstrate significant improvements in policy robustness and task success rates across diverse perturbation scenarios. Our approach provides a scalable, engineering-friendly solution for reliable decision-making in autonomous systems operating under non-ideal actuation conditions.
Technology Category
Application Category
📝 Abstract
Deep reinforcement learning (DRL) has become a powerful tool for complex decision-making in machine learning and AI. However, traditional methods often assume perfect action execution, overlooking the uncertainties and deviations between an agent's selected actions and the actual system response. In real-world applications, such as robotics, mechatronics, and communication networks, execution mismatches arising from system dynamics, hardware constraints, and latency can significantly degrade performance. This work advances AI by developing a novel control-optimized DRL framework that explicitly models and compensates for action execution mismatches, a challenge largely overlooked in existing methods. Our approach establishes a structured two-stage process: determining the desired action and selecting the appropriate control signal to ensure proper execution. It trains the agent while accounting for action mismatches and controller corrections. By incorporating these factors into the training process, the AI agent optimizes the desired action with respect to both the actual control signal and the intended outcome, explicitly considering execution errors. This approach enhances robustness, ensuring that decision-making remains effective under real-world uncertainties. Our approach offers a substantial advancement for engineering practice by bridging the gap between idealized learning and real-world implementation. It equips intelligent agents operating in engineering environments with the ability to anticipate and adjust for actuation errors and system disturbances during training. We evaluate the framework in five widely used open-source mechanical simulation environments we restructured and developed to reflect real-world operating conditions, showcasing its robustness against uncertainties and offering a highly practical and efficient solution for control-oriented applications.
Problem
Research questions and friction points this paper is trying to address.
Addresses action execution mismatches in DRL for real-world systems
Develops control-optimized DRL framework to compensate for uncertainties
Enhances robustness in AI decision-making under real-world conditions
Innovation
Methods, ideas, or system contributions that make the work stand out.
Control-optimized DRL framework for action mismatches
Two-stage process for action and control signal
Training accounts for execution errors and corrections
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