mlsanityData to 14 Oct 2026

Equivariant Reinforcement Learning Frameworks for Quadrotor Low-Level Control

arXiv 2502.20500 ↗First posted 27 Feb 2025cs.RO math.OCcited by 2

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

Abstract (arXiv)

Improving sampling efficiency and generalization capability is critical for the successful data-driven control of quadrotor unmanned aerial vehicles (UAVs) that are inherently unstable. While various reinforcement learning (RL) approaches have been applied to autonomous quadrotor flight, they often require extensive training data, posing multiple challenges and safety risks in practice. To address these issues, we propose data-efficient, equivariant monolithic and modular RL frameworks for quadrotor low-level control. Specifically, by identifying the rotational and reflectional symmetries in quadrotor dynamics and encoding these symmetries into equivariant network models, we remove redundancies of learning in the state-action space. This approach enables the optimal control action learned in one configuration to automatically generalize into other configurations via symmetry, thereby enhancing data efficiency. Experimental results demonstrate that our equivariant approaches significantly outperform their non-equivariant counterparts in terms of learning efficiency and flight performance.

Key facts. Cited by 2 papers in the map (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 23 of its 38 references that are in the map. First posted on arXiv 27 Feb 2025.

Builds on

23 of 38 references.

References · 23

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11 more
  1. 2017Control of a Quadrotor with Reinforcement Learningcited by 92
  2. 2022SO(2)-Equivariant Reinforcement Learningcited by 84
  3. 2022A Benchmark Comparison of Learned Control Policies for Agile Quadrotor Flightcited by 47
  4. 2019Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotorscited by 45
  5. 2019Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learningcited by 44
  6. 2022On-Robot Learning With Equivariant Modelscited by 42
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  11. 2023Multi-Agent Reinforcement Learning for the Low-Level Control of a Quadrotor UAVcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Temporally smoothed incremental model-based heuristic dynamic programming for command-filtered cascaded online learning flight controlcited by 0
  2. 2026Multi-Task Reinforcement Learning of Drone Aerobatics by Exploiting Geometric Symmetriescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Symmetries-enhanced Multi-Agent Reinforcement Learning
  2. 2024Equivariant Reinforcement Learning under Partial Observability
  3. 2024Equivariant Offline Reinforcement Learning
  4. 2025SE(3)-Equivariant Robot Learning and Control: A Tutorial Survey
  5. 2025RAPTOR: A Foundation Policy for Quadrotor Control
  6. 2025Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking
  7. 2024Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance
  8. 2026Adaptive Outer-Loop Control of Quadrotors via Reinforcement Learning