mlsanityData to 7 Oct 2026

Multi-Task Reinforcement Learning for Quadrotors

arXiv 2412.12442 ↗First posted 17 Dec 2024cs.RO cs.LGcited by 12

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Abstract (arXiv)

Reinforcement learning (RL) has shown great effectiveness in quadrotor control, enabling specialized policies to develop even human-champion-level performance in single-task scenarios. However, these specialized policies often struggle with novel tasks, requiring a complete retraining of the policy from scratch. To address this limitation, this paper presents a novel multi-task reinforcement learning (MTRL) framework tailored for quadrotor control, leveraging the shared physical dynamics of the platform to enhance sample efficiency and task performance. By employing a multi-critic architecture and shared task encoders, our framework facilitates knowledge transfer across tasks, enabling a single policy to execute diverse maneuvers, including high-speed stabilization, velocity tracking, and autonomous racing. Our experimental results, validated both in simulation and real-world scenarios, demonstrate that our framework outperforms baseline approaches in terms of sample efficiency and overall task performance.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 26 of its 29 references that are in the map. First posted on arXiv 17 Dec 2024.

Builds on

26 of 29 references.

References · 26

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018On the Continuity of Rotation Representations in Neural Networkscited by 1,186
  3. 2019Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learningcited by 1,168
  4. 2021Conflict-Averse Gradient Descent for Multi-task Learningcited by 441
  5. 2016CAD2RL: Real Single-Image Flight without a Single Real Imagecited by 227
  6. 2020What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Studycited by 157
  7. 2020Multi-Task Reinforcement Learning with Soft Modularizationcited by 138
  8. 2021Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Interventioncited by 90
  9. 2017Control of a Quadrotor with Reinforcement Learningcited by 75
  10. 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 72
  11. 2020Flightmare: A Flexible Quadrotor Simulatorcited by 69
  12. 2021Autonomous Drone Racing with Deep Reinforcement Learningcited by 51
14 more
  1. 2024Sharing Knowledge in Multi-Task Deep Reinforcement Learningcited by 42
  2. 2019Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learningcited by 40
  3. 2021Lifelong Robotic Reinforcement Learning by Retaining Experiencescited by 34
  4. 2022A Benchmark Comparison of Learned Control Policies for Agile Quadrotor Flightcited by 32
  5. 2023Agilicious: Open-Source and Open-Hardware Agile Quadrotor for Vision-Based Flightcited by 24
  6. 2024Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flightcited by 22
  7. 2023Learning to Fly in Secondscited by 18
  8. 2024Demonstrating Agile Flight from Pixels without State Estimationcited by 18
  9. 2023Autonomous Power Line Inspection with Drones via Perception-Aware MPCcited by 14
  10. 2023Contrastive Learning for Enhancing Robust Scene Transfer in Vision-based Agile Flightcited by 11
  11. 2023Collision Avoidance and Navigation for a Quadrotor Swarm Using End-to-end Deep Reinforcement Learningcited by 6
  12. 2024Learning Speed Adaptation for Flight in Cluttercited by 6
  13. 2024LIMT: Language-Informed Multi-Task Visual World Modelscited by 4
  14. 2024Environment as Policy: Learning to Race in Unseen Trackscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 7; 2026: 5.

12 papers.

  1. 2025The Reality Gap in Robotics: Challenges, Solutions, and Best Practicescited by 22
  2. 2025Accelerating Model-Based Reinforcement Learning with State-Space World Modelscited by 10
  3. 2025RAPTOR: A Foundation Policy for Quadrotor Controlcited by 4
  4. 2025CARoL: Context-aware Adaptation for Robot Learningcited by 1
  5. 2026Approximate Imitation Learning for Event-based Quadrotor Flight in Cluttered Environmentscited by 1
  6. 2025Learning Acrobatic Flight from Preferencescited by 0
  7. 2025Leveraging Temporally Extended Behavior Sharing for Multi-task Reinforcement Learningcited by 0
  8. 2025Curriculum-Based Iterative Self-Play for Scalable Multi-Drone Racingcited by 0
  9. 2026Aerial Inspection Behaviors via RL-based Quadrotor Control for Under-canopy Forest Environmentscited by 0
  10. 2026Memory-Efficient Policy Libraries with Low-Rank Adaptation in Reinforcement Learningcited by 0
  11. 2026Physics-Aware End-to-End Deep Reinforcement Learning for Quadcopter Control with Actuator Dynamicscited by 0
  12. 2026Self-Adaptive Learning and Model Predictive Control for Tracking Unknown Dynamics with No Regretcited by 0