ARXIV:2603.09908 · AERIAL ROBOTICS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

NanoBench: A Multi-Task Benchmark Dataset for Nano-Quadrotor System Identification, Control, and State Estimation

arXiv

NanoBench is an open-source benchmark dataset for advancing nano-quadrotor system identification and control.

Blocked on Code›Score6.0Evidence unverified

Opportunity summary

Pain NanoBench is an open-source benchmark dataset for advancing nano-quadrotor system identification and control.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

NanoBench is an open-source benchmark dataset for advancing nano-quadrotor system identification and control. They omit the actuator-level signals required to study nano-scale quadrotors, where low-Reynolds number aerodynamics, coreless DC motor nonlinearities, and severe computational…

METHOD

Full abstract

Existing aerial-robotics benchmarks target vehicles from hundreds of grams to several kilograms and typically expose only high-level state data. They omit the actuator-level signals required to study nano-scale quadrotors, where low-Reynolds number aerodynamics, coreless DC motor nonlinearities, and severe computational constraints invalidate models and controllers developed for larger vehicles. We introduce NanoBench, an open-source multi-task benchmark collected on the commercially available Crazyflie 2.1 nano-quadrotor (takeoff weight 27 g) in a Vicon motion capture arena. The dataset contains over 170 flight trajectories spanning hover, multi-frequency excitation, standard tracking, and aggressive maneuvers across multiple speed regimes. Each trajectory provides synchronized Vicon ground truth, raw IMU data, onboard extended Kalman filter estimates, PID controller internals, and motor PWM commands at 100 Hz, alongside battery telemetry at 10 Hz, aligned with sub-0.5 ms consistency. NanoBench defines standardized evaluation protocols, train/test splits, and open-source baselines for three tasks: nonlinear system identification, closed-loop controller benchmarking, and onboard state estimation assessment. To our knowledge, it is the first public dataset to jointly provide actuator commands, controller internals, and estimator outputs with millimeter-accurate ground truth on a commercially available nano-scale aerial platform.

RESULT

ScienceToStartup currently rates this 6.0/10 on the public viability pass. To our knowledge, it is the first public dataset to jointly provide actuator commands, controller internals, and estimator outputs with millimeter-accurate ground truth on…

WHY NOW

Aerial Robotics moved forward this cycle; last verified April 2026. Public score 6.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score6.0

PainNanoBench is an open-source benchmark dataset for advancing nano-quadrotor system identification and control.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

NanoBench is an open-source benchmark dataset for advancing nano-quadrotor system identification and control.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2603.09908 · AERIAL ROBOTICS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

NanoBench: A Multi-Task Benchmark Dataset for Nano-Quadrotor System Identification, Control, and State Estimation

arXiv

NanoBench is an open-source benchmark dataset for advancing nano-quadrotor system identification and control.

Blocked on Code›Score6.0Evidence unverified

Opportunity summary

Pain NanoBench is an open-source benchmark dataset for advancing nano-quadrotor system identification and control.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Aerial Robotics moved forward this cycle; last verified April 2026. Public score 6.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score6.0

PainNanoBench is an open-source benchmark dataset for advancing nano-quadrotor system identification and control.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

NanoBench is an open-source benchmark dataset for advancing nano-quadrotor system identification and control.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.09908

NanoBench: A Multi-Task Benchmark Dataset for Nano-Quadrotor System Identification, Control, and State Estimation

NanoBench is an open-source benchmark dataset for advancing nano-quadrotor system identification and control.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback31 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

6.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Aerial Robotics moved forward this cycle; last verified April 2026. Public score 6.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
NanoBench is an open-source benchmark dataset for advancing nano-quadrotor system identification and control. They omit the actuator-level signals required to study nano-scale quadrotors, where low-Reynolds number aerodynamics, coreless DC motor nonlinearities, and severe computational constraints invalidate models and controllers developed for larger vehicles.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Existing aerial-robotics benchmarks target vehicles from hundreds of grams to several kilograms and typically expose only high-level state data. They omit the actuator-level signals required to study nano-scale quadrotors, where low-Reynolds number aerodynamics, coreless DC motor nonlinearities, and severe computational constraints invalidate models and controllers developed for larger vehicles.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. To our knowledge, it is the first public dataset to jointly provide actuator commands, controller internals, and estimator outputs with millimeter-accurate ground truth on a commercially available nano-scale aerial platform.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Aerial Robotics moved forward this cycle; last verified April 2026. Public score 6.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Aerial Robotics

Competitors

not indexed

Competitive landscape

NanoBench is an open-source benchmark dataset for advancing nano-quadrotor system identification and control.

Segment

Aerial Robotics

Adoption evidence

No public code link in the paper record yet

Commercial read

6.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.09908 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(31)

Nonlinear System Identification Nano-drone Benchmark

2025R. Busetto, Elia Cereda et al.

POF+MADER: Trajectory Planner in Dynamic Environments With Improved Collision Avoidance

2025Syed Izzat Ullah, José Baca et al.

CrazySim: A Software-in-the-Loop Simulator for the Crazyflie Nano Quadrotor

2024Christian Llanes, Z. Kakish et al.

Learning to Fly in Seconds

2023Jonas Eschmann, Dario Albani et al.

Nonlinear System Identification for Quadrotors with Neural Ordinary Differential Equations

2023Mingqian Wang, Jianshan Zhou et al.

Reaching the limit in autonomous racing: Optimal control versus reinforcement learning

2023Yunlong Song, Angel Romero et al.

Champion-level drone racing using deep reinforcement learning

2023Elia Kaufmann, L. Bauersfeld et al.

Coaxial Modular Aerial System and the Reconfiguration Applications

2023José Baca, Syed Izzat Ullah et al.

Autonomous Navigation and Mapping of Water Channels in a Simulated Environment Using Micro-Aerial Vehicles

2023Syed Izzat Ullah, Abubakr Muhammad

CrazyChoir: Flying Swarms of Crazyflie Quadrotors in ROS 2

2023Lorenzo Pichierri, A. Testa et al.

imitation: Clean Imitation Learning Implementations

2022Adam Gleave, Mohammad Taufeeque et al.

INSANE: Cross-Domain UAV Data Sets with Increased Number of Sensors for developing Advanced and Novel Estimators

2022Christian Brommer, Alessandro Fornasier et al.

Agilicious: Open-source and open-hardware agile quadrotor for vision-based flight

2022Philipp Foehn, Elia Kaufmann et al.

Neural-Fly enables rapid learning for agile flight in strong winds

2022M. O'Connell, Guanya Shi et al.

NTU VIRAL: A visual-inertial-ranging-lidar dataset, from an aerial vehicle viewpoint

2021Thien-Minh Nguyen, Shenghai Yuan et al.

KNODE-MPC: A Knowledge-Based Data-Driven Predictive Control Framework for Aerial Robots

2021K. Y. Chee, Tom Z. Jiahao et al.

Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning

2021Lukas Brunke, Melissa Greeff et al.

NeuroBEM: Hybrid Aerodynamic Quadrotor Model

2021L. Bauersfeld, Elia Kaufmann et al.

Fully Onboard AI-Powered Human-Drone Pose Estimation on Ultralow-Power Autonomous Flying Nano-UAVs

2021D. Palossi, Nicky Zimmerman et al.

Data-Driven MPC for Quadrotors

2021G. Torrente, Elia Kaufmann et al.

Showing 20 of 31 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

none indexed

Extension

Builds On ThisMotion-Specific Battery Health Assessment for Quadrotors Using High-Fidelity Battery Models

5.0

Builds On ThisThink Like a Pilot: Fine-Grained Long-Horizon UAV Navigation

0.0

Commercially relevant

Higher ViabilityHow to Model Your Crazyflie Brushless

8.0

Higher ViabilityBreaking Smooth-Motion Assumptions: A UAV Benchmark for Multi-Object Tracking in Complex and Adverse Conditions

7.0

Higher ViabilityHelixTrack: Event-Based Tracking and RPM Estimation of Propeller-like Objects

7.0

Higher ViabilityApproximate Imitation Learning for Event-based Quadrotor Flight in Cluttered Environments

8.0

Higher ViabilitySpaceSense-Bench: A Large-Scale Multi-Modal Benchmark for Spacecraft Perception and Pose Estimation

8.0

Higher ViabilityQuadFM: Foundational Text-Driven Quadruped Motion Dataset for Generation and Control

8.0

Higher ViabilityBlankSkip: Early-exit Object Detection onboard Nano-drones

7.0

Higher ViabilityPanoAir: A Panoramic Visual-Inertial SLAM with Cross-Time Real-World UAV Dataset

8.0

Conflicting

none indexed

Owned Distribution

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Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2603.09908
Route: /paper/nanobench-a-multi-task-benchmark-dataset-for-nano-quadrotor-system-identification-control-and-state-estimation
Active tab: read
Artifact: nanobench-a-multi-task-benchmark-dataset-for-nano-quadrotor-system-identification-control-and-state-estimation

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/nanobench-a-multi-task-benchmark-dataset-for-nano-quadrotor-system-identification-control-and-state-estimation/paper-pack
REST build passport API/api/v1/paper/nanobench-a-multi-task-benchmark-dataset-for-nano-quadrotor-system-identification-control-and-state-estimation/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
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  "paper_id": "64b4cc1f-78a9-4474-9007-89bdbb972b0c",
  "arxiv_id": "2603.09908",
  "canonical_route": "/paper/nanobench-a-multi-task-benchmark-dataset-for-nano-quadrotor-system-identification-control-and-state-estimation",
  "active_tab": "synced from current hash by the drawer client",
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  "endpoints": {
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    "build_passport": "/api/v1/paper/nanobench-a-multi-task-benchmark-dataset-for-nano-quadrotor-system-identification-control-and-state-estimation/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/nanobench-a-multi-task-benchmark-dataset-for-nano-quadrotor-system-identification-control-and-state-estimation

stale

Proof freshness: stale
Proof status: unverified
Display score: 6/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

NanoBench: A Multi-Task Benchmark Dataset for Nano-Quadrotor System Identification, Control, and State Estimation

Canonical ID nanobench-a-multi-task-benchmark-dataset-for-nano-quadrotor-system-identification-control-and-state-estimation | Route /paper/nanobench-a-multi-task-benchmark-dataset-for-nano-quadrotor-system-identification-control-and-state-estimation

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/nanobench-a-multi-task-benchmark-dataset-for-nano-quadrotor-system-identification-control-and-state-estimation

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2603.09908"
  }
}

source_context

{
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  "mode": "paper",
  "query": "NanoBench: A Multi-Task Benchmark Dataset for Nano-Quadrotor System Identification, Control, and State Estimation",
  "normalized_query": "2603.09908",
  "route": "/paper/nanobench-a-multi-task-benchmark-dataset-for-nano-quadrotor-system-identification-control-and-state-estimation",
  "paper_ref": "nanobench-a-multi-task-benchmark-dataset-for-nano-quadrotor-system-identification-control-and-state-estimation",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: NanoBench: A Multi-Task Benchmark Dataset for Nano-Quadrotor System Identification, Control, and State Estimation

/buildability/nanobench-a-multi-task-benchmark-dataset-for-nano-quadrotor-system-identification-control-and-state-estimation

Watchwatch

Subject: NanoBench: A Multi-Task Benchmark Dataset for Nano-Quadrotor System Identification, Control, and State Estimation

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/nanobench-a-multi-task-benchmark-dataset-for-nano-quadrotor-system-identification-control-and-state-estimation

Paper ref

nanobench-a-multi-task-benchmark-dataset-for-nano-quadrotor-system-identification-control-and-state-estimation

arXiv id

2603.09908

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

0592a27d2ce25466a152c0c995a72cb7ee1e9401757767b809258b570ada7dc3

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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NanoBench: A Multi-Task Benchmark Dataset for Nano-Quadrotor System Identification, Control, and State Estimation

NanoBench: A Multi-Task Benchmark Dataset for Nano-Quadrotor System Identification, Control, and State Estimation

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