Anticipatory Reinforcement Learning for Trajectory Tracking

2026-07-03

AI Summary

This work addresses the limitations of deep reinforcement learning (DRL) in industrial control, where reactive decision-making based solely on current tracking error often leads to lag and overshoot. To mitigate these issues, the authors propose a lightweight predictive control strategy that augments the DRL state space with target velocity and a single-step-ahead reference trajectory, endowing the controller with limited yet effective foresight. Leveraging the Proximal Policy Optimization (PPO) algorithm, they systematically evaluate eight predictive configurations on a one-degree-of-freedom helicopter platform, validating performance through both simulation and zero-shot real-world transfer. Results demonstrate a reduction in mean absolute deviation from 2.73° to 0.31° in simulation, while a simplified configuration achieves the best real-world tracking accuracy of 1.11°, significantly narrowing the sim-to-real gap and indicating that fine-grained prediction is unnecessary—single-step lookahead suffices for optimal performance.

Abstract

Deep reinforcement learning (DRL) in industrial control often suffers from lag and overshoot due to purely reactive control based on the current tracking error. To achieve anticipatory control without high computational overhead, we introduce a predictive formulation that augments the DRL state space with target velocities and future reference horizons. Evaluating eight configurations using proximal policy optimization (PPO) on a 1-degree-of-freedom (1-DoF) helicopter testbed, simulation results showed a 9-fold error reduction, lowering the mean absolute deviation from 2.73° to 0.31°. However, zero-shot transfer to physical hardware revealed a sim-to-real gap. Interestingly, a simpler configuration using a single, further look-ahead horizon matched the real-world top performance of the most complex model (1.11°). Overall, evaluating various combinations of prediction horizons and target velocities demonstrated that highly granular predictive data is not necessarily required for physical transfer.

Problem

• trajectory tracking
• deep reinforcement learning
• anticipatory control
• sim-to-real gap
• industrial control

Innovation

• Anticipatory Reinforcement Learning
• Predictive State Augmentation
• Sim-to-Real Transfer
• Trajectory Tracking
• Proximal Policy Optimization