mlsanityData to 14 Oct 2026

Anticipatory Reinforcement Learning for Trajectory Tracking

arXiv 2607.03132 ↗First posted 3 Jul 2026cs.LG cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

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 2.73deg to 0.31deg. 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 (1.11deg). Overall, evaluating various combinations of prediction horizons and target velocities demonstrated that highly granular predictive data is not necessarily required for physical transfer.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 6 references that are in the map. First posted on arXiv 3 Jul 2026.

Builds on

4 of 6 references.

References · 4

  1. 2016OpenAI Gymcited by 1,736
  2. 2024Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RLcited by 21
  3. 2024Python-Based Reinforcement Learning on Simulink Modelscited by 9
  4. 2025The Crucial Role of Problem Formulation in Real-World Reinforcement Learningcited by 6

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  1. 2026Integrating Physics-Informed Neural Networks for Safe Reinforcement Learning in a 1-DoF Helicopter System
  2. 2024Comparison of Model Predictive Control and Proximal Policy Optimization for a 1-DOF Helicopter System
  3. 2025Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking
  4. 2024The Power of Input: Benchmarking Zero-Shot Sim-To-Real Transfer of Reinforcement Learning Control Policies for Quadrotor Control
  5. 2024Dual Agent Learning Based Aerial Trajectory Tracking
  6. 2026Anticipatory Reinforcement Learning: From Generative Path-Laws to Distributional Value Functions
  7. 2025Simultaneous learning of state-to-state minimum-time planning and control ↗
  8. 2024Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Tasks