Bridging Performance and Generalization in Reinforcement Learning for Agile Flight

Noosaga Placements

• Robot Learningsubfield95%
  The paper focuses on training a reinforcement learning policy for agile flight, which is a core topic in robot learning. The method involves multi-task RL, adaptive task switching, and procedural task generation to improve generalization.
  We propose a framework for zero-shot generalization in agile flight for RL-based drone racing.By combining task-aware switching based on learning progress with a physically informed procedural track generator, the framework produces a fast and robust generalist policy without test-time adaptation.Our method achieves strong zero-shot performance across a wide range of unseen racetracks in the real world, demonstrating a 7.4x improvement in generalization over the state-of-the-art approaches, while maintaining competitive racing speeds.
• Deep Reinforcement Learningframework95%
  The paper uses Proximal Policy Optimization (PPO), which is a deep reinforcement learning algorithm. The framework 'Deep Reinforcement Learning' is appropriate as the paper trains deep neural network policies using RL.
  In contrast to conventional PPO, we train on multiple different tasks in parallelWe integrate the adaptive task switching into the conventional PPO training method using the algorithm defined in Algorithm 2.
• Reinforcement Learningsubfield90%
  The paper uses reinforcement learning (PPO) as the core learning algorithm and addresses generalization in RL, a key topic in the RL subfield.
  While reinforcement learning (RL) has achieved human-level performance in this domain, current methods fail to generalizeIn this work, we propose a framework for zero-shot generalization in agile flight for RL-based drone racing.Our results show that reinforcement learning can achieve strong zero-shot generalization in high-speed drone racing without a fundamental performance trade-off.
• Model-Free Reinforcement Learningframework90%
  The paper uses a model-free RL approach (PPO) without learning a model of the environment dynamics. The framework 'Model-Free Reinforcement Learning' is appropriate.
  We model the problem of ZSG in autonomous drone racing with an Underspecified Partially Observable Markov Decision Process (UPOMDP)In contrast to traditional model-based control approaches, reinforcement learning can directly learn high-performance control policies through interaction with a simulated environment
• Robot Controlsubfield80%
  The paper addresses control of a quadrotor for agile flight, outputting collective thrust and body-rate setpoints. The learned policy directly controls the drone, which falls under robot control.
  At each timestep, the simulated environment provides an observation, and the agent outputs an action a = [c, ω ref ], where c ∈ R is the collective thrust and ω ref ∈ R3 the body-rate setpoints for roll, pitch, and yaw.Our learned RL controller outperforms state-of-the-art model-based racing controllers, while retaining zero-shot deployment capability.
• Learning-Based Robot Controlframework70%
  The paper uses a learned policy for control, which falls under learning-based robot control. The framework is relevant as the paper compares against model-based control methods and demonstrates that RL can outperform them.
  Our learned RL controller outperforms state-of-the-art model-based racing controllers, while retaining zero-shot deployment capability.How does this compare to optimal control methods? Using the drone model parameters from MPCC [6, 30] and MPCC++ [31], our generalist agent completes SplitS in 5.42s, compared with 5.67s for MPCC and 5.38s for MPCC++.
• Mobile Roboticssubfield70%
  The paper deals with autonomous aerial robots (drones) that are mobile robots, and the task is agile flight and drone racing.
  Autonomous drone racing is a fundamentally challenging regime for autonomous aerial robots, requiring time-optimal control while operating under persistent actuation saturation.Our method achieves strong zero-shot performance across a wide range of unseen racetracks in the real world
• Safe Robot Learningframework60%
  The paper addresses safety implicitly by aiming to prevent crashes during generalization, but does not explicitly focus on safe RL methods. The framework is partially relevant as the work deals with safety-critical deployment.
  Existing approaches that improve generalization impose a substantial cost on flight speed: control policies must significantly degrade performance to achieve even modest levels of generalization.Nevertheless, safety-critical deployment requires additional safeguards, including provably safe fallback controllers and more reliable perception under partial gate visibility, lighting variation, and motion blur.

Abstract

Autonomous drone racing is a fundamentally challenging regime for autonomous aerial robots, requiring time-optimal control while operating under persistent actuation saturation. While reinforcement learning (RL) has achieved human-level performance in this domain, current methods fail to generalize; policies trained on specific environments often crash immediately in unseen configurations. This failure reflects the intrinsic difficulty of zero-shot generalization in agile flight, arising from high-dimensional task variation and the tight coupling between safety and performance at high speeds. Existing approaches that improve generalization impose a substantial cost on flight speed: control policies must significantly degrade performance to achieve even modest levels of generalization. In this work, we propose a framework for zero-shot generalization in agile flight for RL-based drone racing. By combining task-aware switching based on learning progress with a physically informed procedural track generator, the framework produces a fast and robust generalist policy without test-time adaptation. Our method achieves strong zero-shot performance across a wide range of unseen racetracks in the real world, demonstrating a 7.4x improvement in generalization over the state-of-the-art approaches, while maintaining competitive racing speeds. We validate our method's results in both simulation and real-world settings, including a challenging vision-based, end-to-end control setting that operates without explicit state estimation, where all prior approaches fail to generalize.

Paper Context