Agile Interception of a Flying Target using Competitive Reinforcement Learning

2026-03-17

AI Summary

This study addresses the challenge of efficiently intercepting highly maneuverable targets using agile drones, formulating the problem as a competitive reinforcement learning task. The authors train end-to-end low-level control policies for both interceptor and target agents directly on a high-fidelity quadrotor dynamics model, utilizing total thrust and body-frame angular rates as control inputs—an approach demonstrated for the first time in such a realistic setting. Leveraging the JAX framework with GPU-based parallelization significantly accelerates training. The proposed method substantially outperforms conventional heuristic strategies in key metrics, including capture rate, capture time, and crash rate. Furthermore, the learned policies successfully transfer to a real-world indoor flight platform, demonstrating strong effectiveness and generalization capability in physical environments.

Technology Category

• Machine Learning: Reinforcement Learning
• Multiagent Systems: Adversarial Agents
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

Abstract

This article presents a solution to intercept an agile drone by another agile drone carrying a catching net. We formulate the interception as a Competitive Reinforcement Learning problem, where the interceptor and the target drone are controlled by separate policies trained with Proximal Policy Optimization (PPO). We introduce a high-fidelity simulation environment that integrates a realistic quadrotor dynamics model and a low-level control architecture implemented in JAX, which allows for fast parallelized execution on GPUs. We train the agents using low-level control, collective thrust and body rates, to achieve agile flights both for the interceptor and the target. We compare the performance of the trained policies in terms of catch rate, time to catch, and crash rate, against common heuristic baselines and show that our solution outperforms these baselines for interception of agile targets. Finally, we demonstrate the performance of the trained policies in a scaled real-world scenario using agile drones inside an indoor flight arena.

Problem

• Agile Interception
• Flying Target
• Competitive Reinforcement Learning
• Quadrotor Dynamics
• Drone Catching

Innovation

• Competitive Reinforcement Learning
• Agile Drone Interception
• High-fidelity Simulation
• JAX-based Control
• Low-level Policy Training