ARXIV:2603.18910 · ROBOTICS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Safety-Guaranteed Imitation Learning from Nonlinear Model Predictive Control for Spacecraft Close Proximity Operations

Alexander Meinert · Niklas Baldauf · Peter Stadler · Alen Turnwald · arXiv

A safety-guaranteed imitation learning framework for spacecraft close proximity control that significantly reduces computation for onboard deployment.

Ship in 2-4 weeks›Score7.0Evidence unverified

Opportunity summary

Pain A safety-guaranteed imitation learning framework for spacecraft close proximity control that significantly reduces computation for onboard deployment.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A safety-guaranteed imitation learning framework for spacecraft close proximity control that significantly reduces computation for onboard deployment. We leverage Control Barrier Functions (CBFs) for safety certificates and Control Lyapunov Functions (CLFs) for stability as…

METHOD

Full abstract

This paper presents a safety-guaranteed, runtime-efficient imitation learning framework for spacecraft close proximity control. We leverage Control Barrier Functions (CBFs) for safety certificates and Control Lyapunov Functions (CLFs) for stability as unified design principles across data generation, training, and deployment. First, a nonlinear Model Predictive Control (NMPC) expert enforces CBF constraints to provide safe reference trajectories. Second, we train a neural policy with a novel CBF-CLF-informed loss and DAgger-like rollouts with curriculum weighting, promoting data-efficiency and reducing future safety filter interventions. Third, at deployment a lightweight one-step CBF-CLF quadratic program minimally adjusts the learned control input to satisfy hard safety constraints while encouraging stability. We validate the approach for ESA-compliant close proximity operations, including fly-around with a spherical keep-out zone and final approach inside a conical approach corridor, using the Basilisk high-fidelity simulator with nonlinear dynamics and perturbations. Numerical experiments indicate stable convergence to decision points and strict adherence to safety under the filter, with task performance comparable to the NMPC expert while significantly reducing online computation. A runtime analysis demonstrates real-time feasibility on a commercial off-the-shelf processor, supporting onboard deployment for safety-critical on-orbit servicing.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. A runtime analysis demonstrates real-time feasibility on a commercial off-the-shelf processor, supporting onboard deployment for safety-critical on-orbit servicing. Code availability is flagged in the…

WHY NOW

Robotics moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.

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

Opportunity summary

Score7.0

PainA safety-guaranteed imitation learning framework for spacecraft close proximity control that significantly reduces computation for onboard deployment.

Evidence0 refs | 0 sources | 17% coverage

Blockerno shell-level blocker reported

Analysis summary

A safety-guaranteed imitation learning framework for spacecraft close proximity control that significantly reduces computation for onboard deployment.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

ARXIV:2603.18910 · ROBOTICS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Safety-Guaranteed Imitation Learning from Nonlinear Model Predictive Control for Spacecraft Close Proximity Operations

Alexander Meinert · Niklas Baldauf · Peter Stadler · Alen Turnwald · arXiv

A safety-guaranteed imitation learning framework for spacecraft close proximity control that significantly reduces computation for onboard deployment.

Ship in 2-4 weeks›Score7.0Evidence unverified

Opportunity summary

Pain A safety-guaranteed imitation learning framework for spacecraft close proximity control that significantly reduces computation for onboard deployment.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Robotics moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.

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

Opportunity summary

Score7.0

PainA safety-guaranteed imitation learning framework for spacecraft close proximity control that significantly reduces computation for onboard deployment.

Evidence0 refs | 0 sources | 17% coverage

Blockerno shell-level blocker reported

Analysis summary

A safety-guaranteed imitation learning framework for spacecraft close proximity control that significantly reduces computation for onboard deployment.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.18910

Safety-Guaranteed Imitation Learning from Nonlinear Model Predictive Control for Spacecraft Close Proximity Operations

A safety-guaranteed imitation learning framework for spacecraft close proximity control that significantly reduces computation for onboard deployment.

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 fallback21 indexednot indexed

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

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

code

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Robotics moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
A safety-guaranteed imitation learning framework for spacecraft close proximity control that significantly reduces computation for onboard deployment. We leverage Control Barrier Functions (CBFs) for safety certificates and Control Lyapunov Functions (CLFs) for stability as unified design principles across data generation, training, and deployment.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
This paper presents a safety-guaranteed, runtime-efficient imitation learning framework for spacecraft close proximity control. We leverage Control Barrier Functions (CBFs) for safety certificates and Control Lyapunov Functions (CLFs) for stability as unified design principles across data generation, training, and deployment.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. A runtime analysis demonstrates real-time feasibility on a commercial off-the-shelf processor, supporting onboard deployment for safety-critical on-orbit servicing. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Robotics moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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

Robotics

Competitors

not indexed

Competitive landscape

A safety-guaranteed imitation learning framework for spacecraft close proximity control that significantly reduces computation for onboard deployment.

Segment

Robotics

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.18910 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

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Bluesky

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PDF

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

References(21)

Controls for Space: a perspective to 2030s and beyond I

2025Martina Mammarella, Simone D'Amico et al.

Safe Physics-informed Machine Learning for Dynamics and Control

2025Ján Drgoňa, Truong X. Nghiem et al.

Model predictive attitude control for CubeSats: feasibility and requirements for in-orbit testing

2025Alen Turnwald, Julian Scharnagl et al.

Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification

2025Rudolf Reiter, Jasper Hoffmann et al.

Close-Proximity Operations Design, Analysis, and Validation for Non-Cooperative Targets with an Application to the ClearSpace-1 Mission

2025J. Vasconcelos, Serena Gaggi et al.

Constrained Control for Autonomous Spacecraft Rendezvous: Learning-Based Time Shift Governor

2024Taehyeun Kim, Robin Inho Kee et al.

Transformer-Based Model Predictive Control: Trajectory Optimization via Sequence Modeling

2024Davide Celestini, Daniele Gammelli et al.

Robust Spacecraft Attitude Reference Tracking using Tube-Based Model Predictive Control

2024Alexander Meinert, N. Baldauf et al.

MPC for Tracking applied to rendezvous with non-cooperative tumbling targets ensuring stability and feasibility

2024J. A. Rebollo, Rafael Vázquez et al.

Transformers for Trajectory Optimization with Application to Spacecraft Rendezvous

2023T. Guffanti, Daniele Gammelli et al.

Data-Driven Safety Filters: Hamilton-Jacobi Reachability, Control Barrier Functions, and Predictive Methods for Uncertain Systems

2023K. P. Wabersich, Andrew J. Taylor et al.

Constraints-Aware Trajectory Tracking Control of Spacecraft Attitude using LPV and MPC

2023A. Turnwald, N. Baldauf

Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots

2022Shamil Mamedov, Rudolf Reiter et al.

End-to-End Imitation Learning with Safety Guarantees using Control Barrier Functions

2022Ryan K. Cosner, Yisong Yue et al.

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

2021Lukas Brunke, Melissa Greeff et al.

Robust Control Barrier Functions under High Relative Degree and Input Constraints for Satellite Trajectories

2021J. Breeden, Dimitra Panagou

Basilisk: A Flexible, Scalable and Modular Astrodynamics Simulation Framework

2020P. Kenneally, Scott Piggott et al.

Control Barrier Functions: Theory and Applications

2019A. Ames, S. Coogan et al.

Terminal Guidance System for Satellite Rendezvous

2012W. H. Clohessy

A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning

2010S. Ross, Geoffrey J. Gordon et al.

Showing 20 of 21 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

Prior WorkLightweight Model Predictive Control for Spacecraft Rendezvous Attitude Synchronization

7.0

Prior WorkSafety-critical Control Under Partial Observability: Reach-Avoid POMDP meets Belief Space Control

7.0

Extension

Builds On ThisTowards Safe Learning-Based Non-Linear Model Predictive Control through Recurrent Neural Network Modeling

5.0

Builds On ThisFORMULA: FORmation MPC with neUral barrier Learning for safety Assurance

4.0

Builds On ThisSafety-Critical Centralized Nonlinear MPC for Cooperative Payload Transportation by Two Quadrupedal Robots

4.0

Builds On ThisConformal Reachability for Safe Control in Unknown Environments

3.0

Builds On ThisSEP-NMPC: Safety Enhanced Passivity-Based Nonlinear Model Predictive Control for a UAV Slung Payload System

3.0

Builds On ThisSafety-Critical Contextual Control via Online Riemannian Optimization with World Models

3.0

Builds On ThisSIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks

5.0

Commercially relevant

Higher ViabilityCN-CBF: Composite Neural Control Barrier Function for Safe Robot Navigation in Dynamic Environments

8.0

Conflicting

none indexed

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Owned Distribution

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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: 2603.18910
Route: /paper/safety-guaranteed-imitation-learning-from-nonlinear-model-predictive-control-for-spacecraft-close-proximity-operations
Active tab: read
Artifact: safety-guaranteed-imitation-learning-from-nonlinear-model-predictive-control-for-spacecraft-close-proximity-operations

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/safety-guaranteed-imitation-learning-from-nonlinear-model-predictive-control-for-spacecraft-close-proximity-operations/paper-pack
REST build passport API/api/v1/paper/safety-guaranteed-imitation-learning-from-nonlinear-model-predictive-control-for-spacecraft-close-proximity-operations/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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  "arxiv_id": "2603.18910",
  "canonical_route": "/paper/safety-guaranteed-imitation-learning-from-nonlinear-model-predictive-control-for-spacecraft-close-proximity-operations",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "safety-guaranteed-imitation-learning-from-nonlinear-model-predictive-control-for-spacecraft-close-proximity-operations",
  "endpoints": {
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    "build_passport": "/api/v1/paper/safety-guaranteed-imitation-learning-from-nonlinear-model-predictive-control-for-spacecraft-close-proximity-operations/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/safety-guaranteed-imitation-learning-from-nonlinear-model-predictive-control-for-spacecraft-close-proximity-operations

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
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

Safety-Guaranteed Imitation Learning from Nonlinear Model Predictive Control for Spacecraft Close Proximity Operations

Canonical ID safety-guaranteed-imitation-learning-from-nonlinear-model-predictive-control-for-spacecraft-close-proximity-operations | Route /paper/safety-guaranteed-imitation-learning-from-nonlinear-model-predictive-control-for-spacecraft-close-proximity-operations

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/safety-guaranteed-imitation-learning-from-nonlinear-model-predictive-control-for-spacecraft-close-proximity-operations

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2603.18910"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "Safety-Guaranteed Imitation Learning from Nonlinear Model Predictive Control for Spacecraft Close Proximity Operations",
  "normalized_query": "2603.18910",
  "route": "/paper/safety-guaranteed-imitation-learning-from-nonlinear-model-predictive-control-for-spacecraft-close-proximity-operations",
  "paper_ref": "safety-guaranteed-imitation-learning-from-nonlinear-model-predictive-control-for-spacecraft-close-proximity-operations",
  "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: Safety-Guaranteed Imitation Learning from Nonlinear Model Predictive Control for Spacecraft Close Proximity Operations

/buildability/safety-guaranteed-imitation-learning-from-nonlinear-model-predictive-control-for-spacecraft-close-proximity-operations

Watchwatch

Subject: Safety-Guaranteed Imitation Learning from Nonlinear Model Predictive Control for Spacecraft Close Proximity Operations

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/safety-guaranteed-imitation-learning-from-nonlinear-model-predictive-control-for-spacecraft-close-proximity-operations

Paper ref

safety-guaranteed-imitation-learning-from-nonlinear-model-predictive-control-for-spacecraft-close-proximity-operations

arXiv id

2603.18910

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

2786b14c219e416c536ba6b292c1ef6f8e8bfb6dd6f525dfb2617324ad0a642e

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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Safety-Guaranteed Imitation Learning from Nonlinear Model Predictive Control for Spacecraft Close Proximity Operations

Safety-Guaranteed Imitation Learning from Nonlinear Model Predictive Control for Spacecraft Close Proximity Operations

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Brief

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