Agile Interception of a Flying Target using Competitive Reinforcement Learning

• URL: http://arxiv.org/abs/2603.16279v1
• Date: Tue, 17 Mar 2026 09:11:23 GMT
• Title: Agile Interception of a Flying Target using Competitive Reinforcement Learning
• Abstract summary: This article presents a solution to intercept an agile drone by another agile drone carrying a catching net.<n>We formulate the interception as a Competitive Reinforcement Learning problem, where the interceptor and the target drone are controlled by separate policies.<n>We introduce a high-fidelity simulation environment that integrates a realistic quadrotor dynamics model and a low-level control architecture implemented in JAX.
• Score: 2.982218441172364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• AgilePE: Autonomous UAV Pursuit-Evasion via Self-Play Reinforcement Learning [31.828745787124316]
  We present AgilePE, a complete system for autonomous UAV pursuit-evasion via self-play reinforcement learning.<n> AgilePE integrates agile low-level control, competitive policy optimization, and sim-to-real deployment in a unified framework.<n>For training, we use competitive self-play with Prioritized Fictitious Self-Play (PFSP) and a diversified opponent pool, enabling agents to improve against historical policies.<n>Real-world experiments reproduce pursuit-evasion tactics observed in simulation, including rapid dodging and flanking, and demonstrate interactive two-agent zero-shot deployment.
  arXiv  Detail & Related papers  (2026-08-14T09:41:16Z)
• Intelligent Multi-UAV Navigation in ITNTNs: A Hierarchical LLM Approach [33.68706070829591]
  Large Language Models (LLMs) excel at semantic reasoning but suffer from high inference latency, rendering them unsuitable for real-time aerodynamic control.<n>A massive cloud-based LLM deployed on a High-Altitude Platform Station manages slow-timescale global load balancing.<n> lightweight edge-LLMs on individual UAVs translate local observations into tactical sub-goals to execute collision-free, handover-aware trajectories.
  arXiv  Detail & Related papers  (2026-07-21T00:34:42Z)
• Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning [0.0]
  This article presents a solution to intercept an agile drone by a team of agile drone carrying catching nets.<n>We formulate the problem as a competitive Multi-Agent Reinforcement Learning (MARL) task.<n>We train the pursuers and the evader using Multi-Agent Proximal Policy Optimization (MAPPO) with Prioritized Fictitious Self Play (PFSP)
  arXiv  Detail & Related papers  (2026-07-07T07:41:09Z)
• Bridging Performance and Generalization in Reinforcement Learning for Agile Flight [26.465258512733126]
  We propose a framework for zero-shot generalization in agile flight for RL-based drone racing.<n>Our method achieves strong zero-shot performance across a wide range of unseen racetracks in the real world.
  arXiv  Detail & Related papers  (2026-06-25T17:54:36Z)
• Learning Reactive Dexterous Grasping via Hierarchical Task-Space RL Planning and Joint-Space QP Control [50.28263951510334]
  We propose a hybrid hierarchical control framework for reactive dexterous grasping.<n>The proposed framework explicitly decouples high-level spatial intent from low-level joint execution.<n>We extensively validate the proposed framework through a rigorous simulation-to-reality pipeline.
  arXiv  Detail & Related papers  (2026-05-05T04:49:38Z)
• ADV-0: Closed-Loop Min-Max Adversarial Training for Long-Tail Robustness in Autonomous Driving [63.980630608984605]
  This paper presents ADV-0, a closed-loop min-max optimization framework that treats the interaction between driving policy (defender) and adversarial agent (attacker) as a zero-sum Markov game.<n>To make this tractable, we cast dynamic adversary evolution as iterative preference learning, efficiently approximating this optimum and offering an algorithm-agnostic solution to the game.<n> Experiments indicate that it effectively exposes diverse safety-critical failures and greatly enhances the generalizability of both learned policies and motion planners against unseen long-tail risks.
  arXiv  Detail & Related papers  (2026-03-16T12:58:31Z)
• FlyTrap: Physical Distance-Pulling Attack Towards Camera-based Autonomous Target Tracking Systems [25.870424901890292]
  We present a new type of attack: distance-pulling attacks (DPA)<n>FlyTrap is a novel physical-world attack framework that employs an adversarial umbrella as a deployable and domain-specific attack vector.<n>Our evaluations include new datasets, metrics, and closed-loop experiments on real-world white-box and even commercial ATT drones, including DJI and HoverAir.
  arXiv  Detail & Related papers  (2025-09-24T17:59:54Z)
• Reinforcement Learning for Decision-Level Interception Prioritization in Drone Swarm Defense [51.736723807086385]
  We present a case study demonstrating the practical advantages of reinforcement learning in addressing this challenge.<n>We introduce a high-fidelity simulation environment that captures realistic operational constraints.<n>Agent learns to coordinate multiple effectors for optimal interception prioritization.<n>We evaluate the learned policy against a handcrafted rule-based baseline across hundreds of simulated attack scenarios.
  arXiv  Detail & Related papers  (2025-08-01T13:55:39Z)
• Learned Controllers for Agile Quadrotors in Pursuit-Evasion Games [42.74003740156243]
  We address the problem of agile 1v1 quadrotor pursuit-evasion, where a pursuer and an evader learn to outmaneuver each other.<n>We propose an Asynchronous Multi-Stage Population-Based (AMSPB) algorithm to tackle these issues.<n>Within this framework, we train neural network controllers that output either velocity commands or body rates with collective thrust.
  arXiv  Detail & Related papers  (2025-06-03T13:19:23Z)
• Cooperative Search and Track of Rogue Drones using Multiagent Reinforcement Learning [8.775925011558995]
  This work considers the problem of intercepting rogue drones targeting sensitive critical infrastructure facilities.<n>A holistic system that can reliably detect, track, and neutralize rogue drones is proposed.
  arXiv  Detail & Related papers  (2025-01-07T16:22:51Z)
• Open-World Drone Active Tracking with Goal-Centered Rewards [62.21394499788672]
  Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations.<n>We propose DAT, the first open-world drone active air-to-ground tracking benchmark.<n>We also propose GC-VAT, which aims to improve the performance of drone tracking targets in complex scenarios.
  arXiv  Detail & Related papers  (2024-12-01T09:37:46Z)
• Efficient Adversarial Training in LLMs with Continuous Attacks [99.5882845458567]
  Large language models (LLMs) are vulnerable to adversarial attacks that can bypass their safety guardrails. We propose a fast adversarial training algorithm (C-AdvUL) composed of two losses. C-AdvIPO is an adversarial variant of IPO that does not require utility data for adversarially robust alignment.
  arXiv  Detail & Related papers  (2024-05-24T14:20:09Z)
• Learning Multi-Pursuit Evasion for Safe Targeted Navigation of Drones [0.0]
  This paper proposes a novel approach, asynchronous multi-stage deep reinforcement learning (AMS-DRL), to train adversarial neural networks. AMS-DRL evolves adversarial agents in a pursuit-evasion game where the pursuers and the evader are asynchronously trained in a bipartite graph way. We evaluate our method in extensive simulations and show that it outperforms baselines with higher navigation success rates.
  arXiv  Detail & Related papers  (2023-04-07T01:59:16Z)
• Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS [70.60975663021952]
  We study blackbox adversarial attacks on network classifiers. We argue that attacker-defender fixed points are themselves general-sum games with complex phase transitions. We show that a continual learning approach is required to study attacker-defender dynamics.
  arXiv  Detail & Related papers  (2021-11-23T23:42:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.