Paper Citation Record · LEDGER

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking

As of 17 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2506.17832.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.17832 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:05:57.819157Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

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verified fuzzy17
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8c6131ea-c98b-48c9-983d-207ba18a83aa · outbound

This paper cites Optuna: A next-generation hyperparameter optimization framework.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Optuna: A next-generation hyperparameter optimization framework

Reference 1

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Observation 2e9bde93-d425-4f83-bfb6-928d8932f7d5 · outbound

This paper cites Neurobem: Hybrid aerodynamic quadrotor model.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Neurobem: Hybrid aerodynamic quadrotor model

Reference 2

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Observation 6f7d754d-853f-4e1c-bfb7-731ea740f906 · outbound

This paper cites Safe controller optimization for quadrotors with gaussian processes.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Safe controller optimization for quadrotors with gaussian processes

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:05:58.584182Z

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Observation 63fc44cb-bd14-44d3-b168-c7297a299328 · outbound

This paper cites Real-time tuning of pid controller based on optimization algorithms for a quadrotor.Aircraft Engineering and Aerospace Technology, 94(3):418–430, 2021.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Real-time tuning of pid controller based on optimization algorithms for a quadrotor.Aircraft Engineering and Aerospace Technology, 94(3):418–430, 2021

Reference 4

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Observation 5ff41b56-a546-4f99-81a8-93ce989a6ce7 · outbound

This paper cites What Matters in Learning A Zero-Shot Sim-to-Real RL Policy for Quadrotor Control? A Comprehensive Study.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking What Matters in Learning A Zero-Shot Sim-to-Real RL Policy for Quadrotor Control? A Comprehensive Study

Reference 5

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Observation 3925faa6-8b88-4293-b189-c9984538b0b0 · outbound

This paper cites Difftune: Auto-tuning through auto-differentiation.IEEE Transactions on Robotics, 2024.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Difftune: Auto-tuning through auto-differentiation.IEEE Transactions on Robotics, 2024

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:05:58.556152Z

Source-reported events for the cited work

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Observation 04264c5f-92d9-4790-a0a3-06c654832172 · outbound

This paper cites The Power of Input: Benchmarking Zero-Shot Sim-To-Real Transfer of Reinforcement Learning Control Policies for Quadrotor Control.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking The Power of Input: Benchmarking Zero-Shot Sim-To-Real Transfer of Reinforcement Learning Control Policies for Quadrotor Control

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:05:58.312319Z

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Observation e68c81d6-1d5b-4240-aea7-fce66c8f25c8 · outbound

This paper cites Learning to fly in seconds.IEEE Robotics and Automa- tion Letters, 2024.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Learning to fly in seconds.IEEE Robotics and Automa- tion Letters, 2024

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:05:58.540464Z

Source-reported events for the cited work

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Observation b8299426-56ed-4006-be61-71a9dd95a17a · outbound

This paper cites Differential flatness of quadrotor dynamics subject to rotor drag for accurate tracking of high-speed trajectories.IEEE Robotics and Automation Letters, 3(2): 620–626, April 2018.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Differential flatness of quadrotor dynamics subject to rotor drag for accurate tracking of high-speed trajectories.IEEE Robotics and Automation Letters, 3(2): 620–626, April 2018

Reference 9

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Source-reported events for the cited work

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Observation 4e1d919d-b709-48c3-beb6-da0ebee9fa65 · outbound

This paper cites System identification of the crazyflie 2.0 nano quadrocopter.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking System identification of the crazyflie 2.0 nano quadrocopter

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:05:58.525800Z

Source-reported events for the cited work

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Observation d1d3d034-b6ec-4b31-8679-07a3f65470e2 · outbound

This paper cites Behavior Alignment via Reward Function Optimization.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Behavior Alignment via Reward Function Optimization

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:05:58.238547Z

Source-reported events for the cited work

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Observation 5049de1d-3433-4a18-84b0-6ad2ecec979b · outbound

This paper cites Datt: Deep adaptive trajectory tracking for quadrotor control.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Datt: Deep adaptive trajectory tracking for quadrotor control

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:05:58.512162Z

Source-reported events for the cited work

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Observation f13a2a52-611e-4167-b6a6-58c2b6c386f4 · outbound

This paper cites A benchmark comparison of learned control policies for agile quadrotor flight.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking A benchmark comparison of learned control policies for agile quadrotor flight

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:05:58.485369Z

Source-reported events for the cited work

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Observation 061c91aa-192b-43ec-a912-da7d2075fe24 · outbound

This paper cites Champion-level drone racing using deep rein- forcement learning.Nature, 620(7976):982–987, 2023.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Champion-level drone racing using deep rein- forcement learning.Nature, 620(7976):982–987, 2023

Reference 14

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Source-reported events for the cited work

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Observation 06ce36ee-c361-4406-9d05-5d54d57ac937 · outbound

This paper cites Geometric tracking control of a quadrotor uav on se (3).

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Geometric tracking control of a quadrotor uav on se (3)

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:05:58.464194Z

Source-reported events for the cited work

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Observation 70512889-ee56-4091-b35a-3212bdc1c086 · outbound

This paper cites Gpu- accelerated robotic simulation for distributed reinforce- ment learning.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Gpu- accelerated robotic simulation for distributed reinforce- ment learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T19:05:57.750525Z

Source-reported events for the cited work

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Observation 5c3f81ca-2b8d-45b5-8ed9-74e1c0fb5371 · outbound

This paper cites Autotune: Controller tuning for high-speed flight.IEEE Robotics and Automation Letters, 7(2): 4432–4439, 2022.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Autotune: Controller tuning for high-speed flight.IEEE Robotics and Automation Letters, 7(2): 4432–4439, 2022

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:05:58.443501Z

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Observation 8bf6dcf2-1d37-448e-95f7-4c9f310de224 · outbound

This paper cites Design, modeling, estimation and control for aerial grasping and manipulation.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Design, modeling, estimation and control for aerial grasping and manipulation

Reference 18

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T19:05:58.220213Z

Source-reported events for the cited work

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Observation ab5dab26-a2e5-4bea-8f4c-19eceb8a7162 · outbound

This paper cites Orbit: A unified simulation framework for interactive robot learning environments.IEEE Robotics and Au- tomation Letters, 8(6):3740–3747, 2023.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Orbit: A unified simulation framework for interactive robot learning environments.IEEE Robotics and Au- tomation Letters, 8(6):3740–3747, 2023

Reference 19

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Source-reported events for the cited work

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Observation a66be79b-e0a1-4e35-b4bc-6c39cbcef2ee · outbound

This paper cites Sim-to- (multi)-real: Transfer of low-level robust control policies to multiple quadrotors.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Sim-to- (multi)-real: Transfer of low-level robust control policies to multiple quadrotors

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:05:58.429833Z

Source-reported events for the cited work

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Observation 4d897dcc-c266-4673-a38a-111279d8659a · outbound

This paper cites Nonlinear pid-type controller for quadrotor trajectory tracking.IEEE/ASME transactions on mechatronics, 23 (5):2436–2447, 2018.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Nonlinear pid-type controller for quadrotor trajectory tracking.IEEE/ASME transactions on mechatronics, 23 (5):2436–2447, 2018

Reference 21

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Observation 3ff7c3c4-647c-4f77-a9b9-f016f2e18ddb · outbound

This paper cites Learning to walk in minutes using massively par- allel deep reinforcement learning.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Learning to walk in minutes using massively par- allel deep reinforcement learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:05:58.402859Z

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Observation 0e9a4deb-f95d-4f94-91b0-07f8a157fa7c · outbound

This paper cites Proximal Policy Optimization Algorithms.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Proximal Policy Optimization Algorithms

Reference 23

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Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Unresolved cited work

Reference 24

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Observation 8018d8d2-6794-466a-b885-1582c3ec883f · outbound

This paper cites A Comparative Study of Nonlinear MPC and Differential-Flatness-Based Control for Quadrotor Agile Flight.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking A Comparative Study of Nonlinear MPC and Differential-Flatness-Based Control for Quadrotor Agile Flight

Reference 25

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Observation 40bee53b-189a-44a5-948b-8c49f1a3a25e · outbound

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Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Unresolved cited work

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T19:05:57.794614Z

Source-reported events for the cited work

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Observation e002a593-a68f-4ef3-9aae-4e955a452e63 · outbound

This paper cites Automatic pid tuning via differential evolution for quadrotor uavs trajectory tracking.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Automatic pid tuning via differential evolution for quadrotor uavs trajectory tracking

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:05:58.362618Z

Source-reported events for the cited work

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Observation 8127ff7d-8ba6-403a-aecd-d47d5fe3a8ec · outbound

This paper cites Dynami- cally feasible task space planning for underactuated aerial manipulators.IEEE Robotics and Automation Letters, 6 (2):3232–3239, 2021.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Dynami- cally feasible task space planning for underactuated aerial manipulators.IEEE Robotics and Automation Letters, 6 (2):3232–3239, 2021

Reference 28

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Source-reported events for the cited work

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Observation a1be043b-3712-4d39-a2ad-3c0b76713e38 · outbound

This paper cites Leveraging Symmetry to Accelerate Learning of Trajectory Tracking Controllers for Free-Flying Robotic Systems.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Leveraging Symmetry to Accelerate Learning of Trajectory Tracking Controllers for Free-Flying Robotic Systems

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:05:57.888640Z

Source-reported events for the cited work

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Observation 99f334f1-5325-485d-998e-ffe8251cf3e5 · outbound

This paper cites Learning a single near-hover position controller for vastly different quadcopters.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking Learning a single near-hover position controller for vastly different quadcopters

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:05:58.350600Z

Source-reported events for the cited work

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Observation cb057a49-f7ef-41e7-a53c-49032c917ec5 · outbound

This paper cites AirPilot: Interpretable PPO-based DRL Auto-Tuned Nonlinear PID Drone Controller for Robust Autonomous Flights.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking AirPilot: Interpretable PPO-based DRL Auto-Tuned Nonlinear PID Drone Controller for Robust Autonomous Flights

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:05:57.869252Z

Source-reported events for the cited work

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Observation 1bf4f1f8-6f70-4958-96db-000d44b9ac0b · outbound

This paper cites A gradient optimization based pid tuning approach on quadrotor.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking A gradient optimization based pid tuning approach on quadrotor

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:05:58.338498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c1a90e8a-1a25-4975-8e6e-9cf0fd43c402 · outbound

This paper cites URL https://proceedings.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking URL https://proceedings

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:05:58.388535Z

Source-reported events for the cited work

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Observation 90de8476-fe0c-418f-bd46-0521837300cf · outbound

This paper cites URL https://proceedings.

Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking URL https://proceedings

Reference 340

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:05:58.498395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.