Pith. sign in

REVIEW 3 major objections 5 minor 91 references

The Safe Trusted Autonomy for Responsible Space Program

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Satellite AI plus safety filter runs in under 2 ms on space processors.

desk verdict Honest program-summary paper: the integration story is real, but the quantitative evidence lives in companion papers, not here. read the letter →

arxiv 2501.05984 v1 pith:5NVIACQC submitted 2025-01-10 eess.SY cs.SY

Kerianne L. Hobbs , Sean Phillips , Michelle Simon , Joseph B. Lyons , Jared Culbertson , Hamilton Scott Clouse , Nathaniel Hamilton , Kyle Dunlap
show 6 more authors
Zachary S. Lippay Joshua Aurand Zachary I. Bell Taleri Hammack Dorothy Ayres Rizza Lim
This is my paper · ORCID
classification eess.SYcs.SY
keywords spacecraftautonomyreinforcementlearningruntimeassurancecontrolbarrierfunctionshuman-autonomyteamingsatelliteproximityoperationsmultiagentinspectionlaboratoryemulation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The Safe Trusted Autonomy for Responsible Space (STARS) program tries to establish that a complete autonomy stack for satellite proximity operations can be built and tested as one system: a reinforcement-learning neural network controller, a run time assurance filter that checks each command and modifies unsafe ones before they reach the spacecraft, and a human interface that lets an operator select agents and adjust safety priorities. The paper's central demonstration is that the neural network and the safety filter run fast enough and robustly enough to close the control loop in a laboratory where aerial drones are made to follow satellite dynamics, with real sensor noise in the loop. It reports maximum execution times below two milliseconds on commercial and radiation-tolerant processors, supporting control at up to ten hertz. The human interface is integrated into the same testbed, but the paper explicitly defers operator studies to future work.

What carries the argument

The load-bearing mechanism is the run time assurance layer built around the Active Set Invariance Filter (ASIF): an online safety filter that, at each step, solves a quadratic program whose constraints are control barrier functions, so the actual control is the smallest modification of the neural network's desired command that keeps the trajectory in the safe set. This is what allows an unverified learning-based primary controller to be used with safety guarantees. The empirical argument is carried by the LINCS testbed, in which aerial drones are commanded in software to follow J2-perturbed two-body dynamics resolved in Hill's frame, giving terrestrial experiments satellite-like relative motion with real noisy sensors and real-time feedback.

What would settle it

Run the integrated controller in closed loop against an independently validated high-fidelity orbital proximity-operations simulator, or on an air-bearing testbed, using the same states, sensor noise, and disturbances as the LINCS experiments, and check whether all the safety constraints remain satisfied; a single violation would show the emulation-based robustness result does not transfer.

Watch

Extended reading notes

Core claim

In the paper's own account, the discovery is that a neural network trained by reinforcement learning can act as the primary controller for multi-satellite inspection while an Active Set Invariance Filter, enforcing a set of safety constraints that spans motion, attitude, power, thermal, and relationships to other objects, keeps every command inside the safe set, and that this combination survives contact with physical hardware. The integrated RL-based controllers, RTA algorithms, and human-AI teaming interface were evaluated in the LINCS lab, where aerial vehicles are forced to follow J2-perturbed two-body dynamics in Hill's frame; the paper states that the combined system exceeded performance expectations under noisy real-time feedback. On spacecraft processors, the trained networks and most run time assurance configurations executed in under two milliseconds, which the paper takes as evidence that training on the ground and deploying at the edge is feasible for neural network spacecraft controllers.

Load-bearing premise

The central load-bearing premise is that the drone emulation, which is digitally forced to follow satellite equations of motion, faithfully represents real close-proximity satellite dynamics well enough that robustness measured in the lab will transfer to orbit.

Editorial extensions

If this is right

  • Trained neural network controllers are viable on current space processors, since inference and the safety-filter optimization stay below two milliseconds, well within a ten-hertz control loop.
  • A single run time assurance filter can enforce many heterogeneous constraints at once, and its computational cost drops when the primary controller is already safe.
  • Continuous control barrier functions are practical for real-time operation on space-grade hardware, while discrete barrier functions remain a fallback for low-frequency filtering.
  • Physical emulation with noisy feedback can serve as a meaningful testbed for integrated autonomy even before on-orbit demonstration, because the platform is forced to follow satellite relative dynamics.
  • Human directability in this architecture is exercised through the safety layer and through pre-trained agent selection; final claims about the interface's effectiveness await operator experiments.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the run time assurance filter is controller-agnostic, the same ASIF safety layer could be applied to human teleoperation or classical guidance without retraining, a step the paper describes as possible but does not demonstrate.
  • The LINCS emulation could be used to quantify sim-to-real transfer: run the same trained agents under deliberately increasing disturbance levels and sensor noise, and record which observation-space choices degrade gracefully, giving a direct test of the robustness boundary the paper asserts.
  • The dynamic multi-objective successor-features controller described in the paper was judged too immature for the interface; once matured, it would let operators change mission-objective weights at run time instead of choosing among pre-trained agents.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This paper presents a program-level overview of the Air Force Research Laboratory's Safe Trusted Autonomy for Responsible Space (STARS) program, a three-year effort to develop and integrate reinforcement learning (RL)-based multi-satellite control, run time assurance (RTA) algorithms based on control barrier functions and active set invariance filters, and a human-AI teaming (HAI) interface. The paper describes the RL training environments for docking and inspection missions, the RTA safety constraint set and filtering architecture, the HAI prototype, and the LINCS laboratory in which aerial drones emulate satellite proximity operations. It also reports deployment of the controllers on space-grade processors in the SPACER lab and attitude safety tests on the Georgia Tech ASTROS platform. The primary claims are that the integrated RL+RTA+HAI system was demonstrated in the LINCS lab on physical vehicles, that it was 'robust to disturbances' and 'exceeded performance expectations,' and that the RL and RTA algorithms run fast enough for real-time spacecraft control. Detailed quantitative results are deferred to companion papers, and the effectiveness of the HAI interface is explicitly left to future work.

Significance. If the STARS results hold, the program provides a useful integration template for learning-enabled autonomous spacecraft: it demonstrates a modular RTA architecture that can bound an RL controller to multiple safety constraints, and the SPACER timing results suggest that such controllers can execute on radiation-tolerant processors at rates suitable for real-time control. The manuscript's value as an archival paper, however, is limited by the absence of any quantitative data. The strengths are the comprehensiveness of the literature survey, the clear architectural descriptions, and the explicit linkage to a substantial body of companion papers that appear to contain the actual experimental details. The paper is best read as a program survey or technical roadmap rather than as a self-contained validation of the integrated system.

major comments (3)
  1. [Section 6 (Integration in LINCS) and Section 7 (Conclusion)] The central claim that the integrated RL+RTA+HAI system 'exceeded performance expectations' and was 'robust to disturbances' is not supported by data in this manuscript. The closed-loop LINCS test is described only qualitatively: no tracking-error statistics, no noise characterization, and no comparison of physical-vehicle error to the scaled safety margins (e.g., safe separation or keep-in-zone) used by the RTA. Because the CBF/ASIF guarantees in Eqs. (1)-(3) apply to the simulated satellite state propagated by the dynamics node, not to the physical drone state, the transfer of these guarantees to the real platform requires explicit quantitative evidence. I recommend either providing summary statistics from [88] in this paper or restating the conclusion to limit the robustness claim to the simulated dynamics.
  2. [Section 5 and Section 7 (Conclusion)] The conclusion's 'combined system exceeded performance expectations' conflates successful integration with demonstrated human-AI teaming effectiveness. The paper itself states in Section 5 and the abstract that operator studies are left to future work, and no usability, workload, or trust metrics are reported. As written, the conclusion overstates the evidence; it should be rephrased to say that the interface was integrated and functioned as a control surface, while teaming effectiveness remains unmeasured.
  3. [Sections 3, 4, and 6] The manuscript's primary empirical results are deferred to companion papers [88] and [90], so the reader cannot verify the claimed RL performance, RTA safety satisfaction, or the SPACER timing ('maximum observed execution time well below 2 ms'). Since the paper presents itself as reporting 'the primary results' of the program, it should include at least summary tables of the key metrics (e.g., task completion rates, constraint violation counts, execution times) or clearly label this as an overview with all quantitative results published elsewhere.
minor comments (5)
  1. [Section 1 (Introduction)] The phrase 'increase scientific discovery the pace of scientific discovery' contains a duplicated and incomplete construction; it should likely read 'increase the pace of scientific discovery.'
  2. [Section 4 and Appendix I] The Introduction states that RTA assures '14 different safety constraints,' but Section 4 enumerates 11 constraints, and the Appendix defines 'STAR Space Trusted Autonomy Readiness' and 'STARS Safe Trusted Autonomy for Responsible Spacecraft' while the title uses 'Responsible Space.' Please reconcile the constraint count and the acronym definitions.
  3. [Equation (4)] The notation phi_u_des_1(x) in the switching filter is not defined; specify whether it denotes the one-step or fixed-horizon flow of the system under the desired controller.
  4. [Section 6 (Laboratory Development, Integration, and Testing)] The term 'Class-I aerial vehicles' is used without definition; either explain the class or use a more generic descriptor.
  5. [Figure 10] The captions '(a) LINCS Simulation Environment' and '(b) LINCS Emulation Environment' would be clearer if they indicated that these are software block diagrams, and the data flow paths between the simulation and emulation environments could be labeled directly in the figure.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claims are empirical integration demonstrations, and the paper's mathematical content consists of standard CBF/ASIF safety-filter definitions rather than predictions derived from fitted inputs.

full rationale

This paper is a program-level integration summary rather than a derivation-based paper. The only mathematical objects are the standard switching/ASIF and CBF definitions (Eqs. 1-5), which are used as safety filters by construction; the paper does not claim to derive a novel behavioral prediction from them. The RL agents are trained and evaluated in the same CWH/Hill's-frame environment, but using the same simulator for training and evaluation is not circular by itself. Detailed performance statistics are deferred to companion papers [88] and [90], which are self-citations; however, under the stated rules, self-citation is not circularity when the cited work supplies external, falsifiable measurements (drone-based integration tests and processor timing) rather than parameters fitted to the present paper's conclusions. The conclusion that the 'combined system exceeded performance expectations' is qualitative and not supported by data in this manuscript, and the LINCS drone emulation fidelity is not quantitatively validated; those are evidence-sufficiency and external-validity concerns, not reductions of a claimed result to its own inputs by construction. No load-bearing uniqueness theorem, ansatz-smuggled-via-citation, or renaming of a known result as new was found. Therefore no circular steps are identified.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper's central claims rest on inherited control-theoretic tools (CBF/ASIF, RTA switching) and on the fidelity of the drone emulation, rather than on free parameters fitted in this paper. No invented physical entities are introduced. The main burden is the set of domain assumptions: that safety can be assured by filtering, that the 11 constraints capture the hazards, and that the LINCS emulation transfers to orbit.

assumptions (3)
  • domain assumption Safety can be assured by filtering an unverified primary controller with CBF-based ASIF plus switching backup.
    The paper adopts RTA as the safety layer and relies on CBF invariance theory and backup controllers to guarantee constraint satisfaction; no proof of the combined switching/ASIF scheme is provided here.
  • domain assumption The drone-based LINCS emulation, with drones forced to follow J2-perturbed two-body dynamics in Hill's frame scaled to the lab, is a faithful proxy for on-orbit close-proximity operations.
    Section 6 describes the emulation approach but provides no quantitative comparison to flight data or higher-fidelity orbital simulators.
  • domain assumption The 11 listed safety constraints (separation, speed, keep-in-zone, passive safety, attitude exclusion, communication, thermal, power, angular velocity, fuel) capture the hazards relevant to autonomous inspection.
    Section 4 states they were elicited from a risk-based hazard analysis, but the formal interpretation varied across implementations and was not validated against operational missions.

how reviews work

0 comments
Cite this review

Pith. "Pith review of The Safe Trusted Autonomy for Responsible Space Program." pith.science (2026). https://pith.science/paper/5NVIACQC

@misc{pith2026250105984,
  author       = {Pith},
  title        = {Pith review of: The Safe Trusted Autonomy for Responsible Space Program},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5NVIACQC}},
  note         = {Machine review of arXiv:2501.05984}
}
read the original abstract

The Safe Trusted Autonomy for Responsible Space (STARS) program aims to advance autonomy technologies for space by leveraging machine learning technologies while mitigating barriers to trust, such as uncertainty, opaqueness, brittleness, and inflexibility. This paper presents the achievements and lessons learned from the STARS program in integrating reinforcement learning-based multi-satellite control, run time assurance approaches, and flexible human-autonomy teaming interfaces, into a new integrated testing environment for collaborative autonomous satellite systems. The primary results describe analysis of the reinforcement learning multi-satellite control and run time assurance algorithms. These algorithms are integrated into a prototype human-autonomy interface using best practices from human-autonomy trust literature, however detailed analysis of the effectiveness is left to future work. References are provided with additional detailed results of individual experiments.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

91 extracted references · 77 canonical work pages

  1. [88]

    Demonstrating reinforce- ment learning and run time assurance for spacecraft inspection using unmanned aerial vehicles,

    K. Dunlap, N. Hamilton, Z. Lippay, M. Shubert, S. Phillips, and K. L. Hobbs, “Demonstrating reinforce- ment learning and run time assurance for spacecraft inspection using unmanned aerial vehicles,” in AIAA SCITECH 2025 Forum, 2025, pp. 1–19

    work page 2025

  2. [90]

    Space processor computa- tion time analysis for reinforcement learning and run time assurance control policies,

    N. Hamilton, K. Dunlap, F. Viramontes, D. Landauer, E. Kain, and K. L. Hobbs, “Space processor computa- tion time analysis for reinforcement learning and run time assurance control policies,” in AIAA SCITECH 2025 Forum, 2025, pp. 1–18

    work page 2025

  3. [1]

    Results from the deep space 1 technology validation mission,

    M. D. Rayman, P. Varghese, D. H. Lehman, and L. L. Livesay, “Results from the deep space 1 technology validation mission,” Acta Astronautica, vol. 47, no. 2- 9, pp. 475–487, 2000. 11 AEGIS Autonomous Exploration for Gathering Increased Science AI Artificial Intelligence ASIF Active Set Invariance Filter ASTERIA Arcsecond Space Telescope Enabling Research ...

    2000

  4. [2]

    Autonomous navigation for deep space missions,

    S. Bhaskaran, “Autonomous navigation for deep space missions,” in SpaceOps 2012, 2012, p. 1267135

    2012

  5. [3]

    Deep impact autonomous navigation: the trials of targeting the unknown,

    D. G. Kubitschek, N. Mastrodemos, R. A. Werner, B. M. Kennedy, S. P. Synnott, G. W. Null, S. Bhaskaran, J. E. Riedel, and A. T. Vaughan, “Deep impact autonomous navigation: the trials of targeting the unknown,” in 29th Annual AAS Rocky Mountain Guidance and Control Conference. American Astronautical Society, 2006, p. 381—406

    2006

  6. [4]

    Results from the asteria cubesat extended mission experiments,

    L. Fesq, P. Beauchamp, C. Altenbuchner, R. Bocchino, A. Donner, M. Feather, K. Hughes, B. Kennedy, R. Mackey, F. Mirza et al. , “Results from the asteria cubesat extended mission experiments,” in 2021 IEEE Aerospace Conference (50100) . IEEE, 2021, pp. 1– 11

    2021

  7. [5]

    Artemis i optical navigation system per- formance,

    R. Inman, G. Holt, J. Christian, K. W. Smith, and C. D’Souza, “Artemis i optical navigation system per- formance,” in AIAA SCITECH 2024 Forum , 2024, pp. 1–20

    2024

  8. [6]

    Overview of the dart mishap investigation results,

    S. Croomes, “Overview of the dart mishap investigation results,” NASA Report, pp. 1–10, 2006

    2006

Show all 91 references
  1. [7]

    Orbital express program summary and mission overview,

    R. B. Friend, “Orbital express program summary and mission overview,” in Sensors and Systems for space applications II, vol. 6958. SPIE, 2008, pp. 11–21

    2008

  2. [8]

    Design through op- eration of an image-based velocity estimation system for mars landing,

    A. Johnson, R. Willson, Y . Cheng, J. Goguen, C. Leger, M. Sanmartin, and L. Matthies, “Design through op- eration of an image-based velocity estimation system for mars landing,” International Journal of Computer Vision, vol. 74, no. 3, pp. 319–341, 2007

    2007

  3. [9]

    Mars 2020 lander vision system flight performance,

    A. E. Johnson, S. B. Aaron, H. Ansari, C. Bergh, H. Bourdu, J. Butler, J. Chang, R. Cheng, Y . Cheng, K. Clark et al., “Mars 2020 lander vision system flight performance,” in AIAA SciTech 2022 Forum , 2022, p. 1214

    2020

  4. [10]

    Aegis autonomous targeting for chem- cam on mars science laboratory: Deployment and re- sults of initial science team use,

    R. Francis, T. Estlin, G. Doran, S. Johnstone, D. Gaines, V . Verma, M. Burl, J. Frydenvang, S. Monta ˜no, R. Wiens et al., “Aegis autonomous targeting for chem- cam on mars science laboratory: Deployment and re- sults of initial science team use,” Science Robotics , vol. 2, n...

    2017

  5. [11]

    Autonomous robotics is driving perseverance rover’s progress on mars,

    V . Verma, M. W. Maimone, D. M. Gaines, R. Francis, T. A. Estlin, S. R. Kuhn, G. R. Rabideau, S. A. Chien, M. M. McHenry, E. J. Graser et al. , “Autonomous robotics is driving perseverance rover’s progress on mars,” Science Robotics , vol. 8, no. 80, p. eadi3099, 2023

    2023

  6. [12]

    F. L. Markley and J. L. Crassidis, Fundamen- tals of spacecraft attitude determination and control . Springer, 2014, vol. 1286

    2014

  7. [13]

    Terminal guidance sys- tem for satellite rendezvous,

    W. Clohessy and R. Wiltshire, “Terminal guidance sys- tem for satellite rendezvous,” Journal of the Aerospace Sciences, vol. 27, no. 9, pp. 653–658, 1960

    1960

  8. [14]

    Researches in the lunar theory,

    G. W. Hill, “Researches in the lunar theory,” American journal of Mathematics, vol. 1, no. 1, pp. 5–26, 1878

  9. [15]

    Openai gym,

    G. Brockman, V . Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” 2016

    2016

  10. [16]

    A spacecraft benchmark problem for hybrid control and estimation,

    C. Jewison and R. S. Erwin, “A spacecraft benchmark problem for hybrid control and estimation,” in 2016 IEEE 55th Conference on Decision and Control (CDC). IEEE, 2016, pp. 3300–3305

    2016

  11. [17]

    Challenge problem: Assured satellite proximity op- erations,

    C. D. Petersen, K. Hobbs, K. Lang, and S. Phillips, “Challenge problem: Assured satellite proximity op- erations,” in 31st AAS/AIAA Space Flight Mechanics Meeting, 2021, p. 1

    2021

  12. [18]

    Safe reinforcement learning benchmark environments for aerospace control systems,

    U. J. Ravaioli, J. Cunningham, J. McCarroll, V . Gan- gal, K. Dunlap, and K. L. Hobbs, “Safe reinforcement learning benchmark environments for aerospace control systems,” in2022 IEEE Aerospace Conference (AERO). IEEE, 2022, pp. 1–20

    2022

  13. [19]

    Comparing the explainability and performance of reinforcement learning and genetic fuzzy systems for safe satellite docking,

    K. Dunlap, K. Cohen, and K. Hobbs, “Comparing the explainability and performance of reinforcement learning and genetic fuzzy systems for safe satellite docking,” in Explainable AI and Other Applications of Fuzzy Techniques: Proceedings of the 2021 Annual Conference of the Nort...

    2021

  14. [20]

    Planning autonomous spacecraft rendezvous and docking trajec- tories via reinforcement learning,

    S. A. P. Vincent Chen and D. A. Copp, “Planning autonomous spacecraft rendezvous and docking trajec- tories via reinforcement learning,” in45th Rocky Moun- tain AAS GN&C Conference, 2023. 12

    2023

  15. [21]

    Proximal policy optimization algorithms,

    J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” 2017

    2017

  16. [22]

    Deep reinforcement learning for autonomous spacecraft in- spection using illumination,

    D. van Wijk, K. Dunlap, M. Majji, and K. Hobbs, “Deep reinforcement learning for autonomous spacecraft in- spection using illumination,” AAS/AIAA Astrodynamics Specialist Conference, Big Sky, Montana, 2023

    2023

  17. [23]

    Run time assured reinforcement learning for six degree-of-freedom spacecraft inspection,

    K. Dunlap, K. Bennett, D. van Wijk, N. Hamilton, and K. Hobbs, “Run time assured reinforcement learning for six degree-of-freedom spacecraft inspection,” in AIAA ASCEND 2024, July 2024

    2024

  18. [24]

    Deep reinforcement learning for scalable multiagent space- craft inspection,

    K. Dunlap, N. Hamilton, and K. L. Hobbs, “Deep reinforcement learning for scalable multiagent space- craft inspection,” in AAS/AIAA Spaceflight Mechanics Meeting, Kaua’i, Hi, 2025

    2025

  19. [25]

    Ablation study of how run time assurance impacts the training and performance of reinforcement learning agents,

    N. Hamilton, K. Dunlap, T. T. Johnson, and K. L. Hobbs, “Ablation study of how run time assurance impacts the training and performance of reinforcement learning agents,” in 2023 IEEE 9th International Con- ference on Space Mission Challenges for Information Technology (SMC-IT)...

    2023

  20. [26]

    Investigat- ing the impact of choice on deep reinforcement learning for space controls,

    N. Hamilton, K. Dunlap, and K. L. Hobbs, “Investigat- ing the impact of choice on deep reinforcement learning for space controls,” in 2024 IEEE 10th International Conference on Space Mission Challenges for Informa- tion Technology (SMC-IT). IEEE, 2024, pp. 1–18

    2024

  21. [27]

    Investigating the impact of observation space design choices on training reinforcement learning solu- tions for spacecraft problems,

    ——, “Investigating the impact of observation space design choices on training reinforcement learning solu- tions for spacecraft problems,” inAAS/AIAA Spaceflight Mechanics Meeting, Kaua’i, Hi, 2025

    2025

  22. [28]

    Deep q-learning for decentralized multi-agent inspec- tion of a tumbling target,

    J. Aurand, S. Cutlip, H. Lei, K. Lang, and S. Phillips, “Deep q-learning for decentralized multi-agent inspec- tion of a tumbling target,” Journal of Spacecraft and Rockets, vol. 61, no. 2, pp. 341–354, 2024

    2024

  23. [29]

    Deep reinforcement learning for multi- agent autonomous satellite inspection,

    H. H. Lei, M. Shubert, N. Damron, K. Lang, and S. Phillips, “Deep reinforcement learning for multi- agent autonomous satellite inspection,” in Proceedings of the 44th Annual American Astronautical Society Guidance, Navigation, and Control Conference, 2022 , M. Sandnas and D. B...

    2022

  24. [30]

    Exposure-based multi-agent inspection of a tumbling target using deep reinforcement learning,

    J. Aurand, S. Cutlip, H. Lei, K. Lang, and S. Phillips, “Exposure-based multi-agent inspection of a tumbling target using deep reinforcement learning,” in45th Rocky Mountain AAS GN&C Conference, 2023

    2023

  25. [31]

    Stacked universal successor feature approximators for safety in reinforcement learning,

    I. Cannon, W. Garcia, T. Gresavage, J. Saurine, I. Leong, and J. Culbertson, “Stacked universal successor feature approximators for safety in reinforcement learning,” 2024. [Online]. Available: https://arxiv.org/abs/2409.04641

    2024 arXiv

  26. [32]

    Uni- versal value function approximators,

    T. Schaul, D. Horgan, K. Gregor, and D. Silver, “Uni- versal value function approximators,” inProceedings of the 32nd International Conference on Machine Learn- ing, ser. Proceedings of Machine Learning Research, F. Bach and D. Blei, Eds., vol. 37. Lille, France: PMLR, 07 2015...

    2015

  27. [33]

    Run time assurance for autonomous spacecraft inspection,

    K. Dunlap, D. van Wijk, and K. L. Hobbs, “Run time assurance for autonomous spacecraft inspection,” AAS/AIAA Astrodynamics Specialist Conference, Big Sky, Montana, 2023

    2023

  28. [34]

    Approximate optimal indirect regulation of an unknown agent with a lyapunov-based deep neural network,

    W. Makumi, Z. I. Bell, and W. E. Dixon, “Approximate optimal indirect regulation of an unknown agent with a lyapunov-based deep neural network,” Control Systems Letters, vol. 7, pp. 2773–2778, 2023

    2023

  29. [35]

    Safe and reliable training of learning-based aerospace controllers,

    U. Mandal, G. Amir, H. Wu, I. Daukantas, F. L. Newell, U. Ravaioli, B. Meng, M. Durling, K. Hobbs, M. Ganai et al. , “Safe and reliable training of learning-based aerospace controllers,” arXiv preprint arXiv:2407.07088, 2024

    2024 arXiv

  30. [36]

    Runtime assurance for safety-critical systems: An introduction to safety filtering approaches for complex control systems,

    K. L. Hobbs, M. L. Mote, M. C. Abate, S. D. Coogan, and E. M. Feron, “Runtime assurance for safety-critical systems: An introduction to safety filtering approaches for complex control systems,” IEEE Control Systems Magazine, vol. 43, no. 2, pp. 28–65, 2023

    2023

  31. [37]

    Run time assurance for intelligent aerospace control systems,

    K. Dunlap, “Run time assurance for intelligent aerospace control systems,” Master’s thesis, University of Cincinnati, 2022

    2022

  32. [38]

    The simplex architecture for safe online control system up- grades,

    D. Seto, B. Krogh, L. Sha, and A. Chutinan, “The simplex architecture for safe online control system up- grades,” in Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207) , vol. 6, 1998, pp. 3504–3508 vol.6

    1998

  33. [39]

    An online approach to active set invariance,

    T. Gurriet, M. Mote, A. D. Ames, and E. Feron, “An online approach to active set invariance,” in 2018 IEEE Conference on Decision and Control (CDC) . IEEE, 2018, pp. 3592–3599

    2018

  34. [40]

    Control barrier functions: Theory and applications,

    A. D. Ames, S. Coogan, M. Egerstedt, G. Notomista, K. Sreenath, and P. Tabuada, “Control barrier functions: Theory and applications,” in 2019 18th European Con- trol Conference (ECC). IEEE, 2019, pp. 3420–3431

    2019

  35. [41]

    Com- paring run time assurance approaches for safe spacecraft docking,

    K. Dunlap, M. Hibbard, M. Mote, and K. Hobbs, “Com- paring run time assurance approaches for safe spacecraft docking,” IEEE Control Systems Letters , vol. 6, pp. 1849–1854, 2021

    2021

  36. [42]

    Run time assured reinforcement learning for safe satellite docking,

    K. Dunlap, M. Mote, K. Delsing, and K. L. Hobbs, “Run time assured reinforcement learning for safe satellite docking,” Journal of Aerospace Information Systems , vol. 20, no. 1, pp. 25–36, 2023

    2023

  37. [43]

    A universal framework for generalized run time assurance with jax automatic differentiation,

    U. J. Ravaioli, K. Dunlap, and K. Hobbs, “A universal framework for generalized run time assurance with jax automatic differentiation,” in 2023 American Control Conference (ACC). IEEE, 2023, pp. 4264–4269

    2023

  38. [44]

    JAX: composable transformations of Python+NumPy programs,

    J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, and Q. Zhang, “JAX: composable transformations of Python+NumPy programs,” 2018. [Online]. Available: http://github. com/google/jax

    2018

  39. [45]

    Safe and constrained rendezvous, proximity opera- tions, and docking,

    C. Petersen, R. J. Caverly, S. Phillips, and A. Weiss, “Safe and constrained rendezvous, proximity opera- tions, and docking,” in 2023 American Control Confer- ence (ACC), 2023, pp. 3645–3661

    2023

  40. [46]

    Elicitation and formal specification of run time assurance requirements for aerospace collision avoidance systems

    K. Hobbs, “Elicitation and formal specification of run time assurance requirements for aerospace collision avoidance systems.” Ph.D. dissertation, Georgia Insti- tute of Technology, Atlanta, GA, USA, 2020

    2020

  41. [47]

    Risk- based formal requirement elicitation for automatic spacecraft maneuvering,

    K. L. Hobbs, A. R. Collins, and E. M. Feron, “Risk- based formal requirement elicitation for automatic spacecraft maneuvering,” in AIAA Scitech 2021 Forum, 2021, p. 1122

    2021

  42. [48]

    Formal specification and analysis of spacecraft collision avoid- ance run time assurance requirements,

    K. L. Hobbs, J. Davis, L. Wagner, and E. Feron, “Formal specification and analysis of spacecraft collision avoid- ance run time assurance requirements,” in 2021 IEEE Aerospace Conference (50100) . IEEE, 2021, pp. 1– 16. 13

    2021

  43. [49]

    Guaranteeing safety via active-set invariance filters for multi-agent space systems with coupled dynamics,

    M. Hibbard, U. Topcu, and K. Hobbs, “Guaranteeing safety via active-set invariance filters for multi-agent space systems with coupled dynamics,” in 2022 Ameri- can Control Conference (ACC). IEEE, 2022, pp. 430– 436

    2022

  44. [50]

    Run time assurance for simulta- neous constraint satisfaction during spacecraft attitude maneuvering,

    C.-K. McQuinn, K. Dunlap, N. Hamilton, J. Wilson, and K. L. Hobbs, “Run time assurance for simulta- neous constraint satisfaction during spacecraft attitude maneuvering,” IEEE Aerospace Conference, Big Sky, Montana, 2024

    2024

  45. [51]

    Natural motion-based trajectories for automatic spacecraft collision avoidance during prox- imity operations,

    M. L. Mote, C. W. Hays, A. Collins, E. Feron, and K. L. Hobbs, “Natural motion-based trajectories for automatic spacecraft collision avoidance during prox- imity operations,” in 2021 IEEE Aerospace Conference (50100). IEEE, 2021, pp. 1–12

    2021

  46. [52]

    Run time assurance for spacecraft attitude control under nondeterministic assumptions,

    M. Abate, M. Mote, M. Dor, C. Klett, S. Phillips, K. Lang, P. Tsiotras, E. Feron, and S. Coogan, “Run time assurance for spacecraft attitude control under nondeterministic assumptions,” IEEE Transactions on Control Systems Technology, vol. 32, no. 3, pp. 862– 873, 2024

    2024

  47. [53]

    Safe spacecraft inspection via deep reinforcement learning and discrete control barrier functions,

    D. van Wijk, K. Dunlap, M. Majji, and K. Hobbs, “Safe spacecraft inspection via deep reinforcement learning and discrete control barrier functions,” Journal of Aerospace Information Systems , vol. 0, no. 0, pp. 1–17, 0. [Online]. Available: https://doi.org/10.2514/1. I011391

    doi

  48. [54]

    Fault tolerant run time assurance with control barrier functions for rigid body spacecraft rotation,

    D. van Wijk, M. Majji, and K. L. Hobbs, “Fault tolerant run time assurance with control barrier functions for rigid body spacecraft rotation,” AIAA SCITECH 2024 Forum, 2024

    2024

  49. [55]

    Disturbance-robust backup control barrier functions: Safety under uncertain dynamics,

    D. E. van Wijk, S. Coogan, T. G. Molnar, M. Majji, and K. L. Hobbs, “Disturbance-robust backup control barrier functions: Safety under uncertain dynamics,” IEEE Control Systems Letters, pp. 1–1, 2024

    2024

  50. [56]

    Sensor safety and multi-objective satellite control under nonlinear dynamics,

    K. Miller, J. M. Brewer, A. A. Soderlund, and S. Phillips, “Sensor safety and multi-objective satellite control under nonlinear dynamics,” in 2023 American Control Conference (ACC), 2023, pp. 4284–4289

    2023

  51. [57]

    Guaranteed safe satellite guidance and naviga- tion using reachability-based switching controllers,

    ——, “Guaranteed safe satellite guidance and naviga- tion using reachability-based switching controllers,” in 2024 American Control Conference (ACC), 2024

    2024

  52. [58]

    Trajectory synthesis for the coordinated inspection of a spacecraft with safety guar- antees,

    M. Hibbard, M. Cubuktepe, M. Shubert, K. Lang, U. Topcu, and S. Phillips, “Trajectory synthesis for the coordinated inspection of a spacecraft with safety guar- antees,” Journal of Guidance, Control, and Dynamics , vol. 46, no. 12, pp. 2245–2264, 2023

    2023

  53. [59]

    Autonomous satellite rendezvous and proximity oper- ations with time-constrained sub-optimal model predic- tive control,

    G. Behrendt, A. Soderlund, M. Hale, and S. Phillips, “Autonomous satellite rendezvous and proximity oper- ations with time-constrained sub-optimal model predic- tive control,” IFAC-PapersOnLine, vol. 56, no. 2, pp. 9380–9385, 2023, 22nd IFAC World Congress

    2023

  54. [60]

    Adaptable (not adaptive) automation: Forefront of human–automation teaming,

    G. Calhoun, “Adaptable (not adaptive) automation: Forefront of human–automation teaming,” Human Fac- tors, vol. 64, no. 2, pp. 269–277, 2022

    2022

  55. [61]

    Responsible (use of) ai,

    J. B. Lyons, K. Hobbs, S. Rogers, and S. H. Clouse, “Responsible (use of) ai,”Frontiers in Neuroer- gonomics, vol. 4, p. 1201777, 2023

    2023

  56. [62]

    Individualized mutual adaptation in human-agent teams,

    H. Li, T. Ni, S. Agrawal, F. Jia, S. Raja, Y . Gui, D. Hughes, M. Lewis, and K. Sycara, “Individualized mutual adaptation in human-agent teams,” IEEE Trans- actions on Human-Machine Systems, vol. 51, no. 6, pp. 706–714, 2021

    2021

  57. [63]

    Human directability of agents,

    K. L. Myers and D. N. Morley, “Human directability of agents,” in Proceedings of the 1st International Confer- ence on Knowledge Capture, 2001, pp. 108–115

    2001

  58. [64]

    Situa- tion awareness-based agent transparency and human- autonomy teaming effectiveness,

    J. Y . Chen, S. G. Lakhmani, K. Stowers, A. R. Selkowitz, J. L. Wright, and M. Barnes, “Situa- tion awareness-based agent transparency and human- autonomy teaming effectiveness,” Theoretical Issues in Ergonomics Science, vol. 19, no. 3, pp. 259–282, 2018

    2018

  59. [65]

    Human-autonomy teaming: Definitions, debates, and directions,

    J. B. Lyons, K. Sycara, M. Lewis, and A. Capiola, “Human-autonomy teaming: Definitions, debates, and directions,” Frontiers in Psychology, vol. 12, p. 589585, 2021

    2021

  60. [66]

    Space trusted autonomy readiness levels,

    K. L. Hobbs, J. B. Lyons, M. S. Feather, B. P. Bycroft, S. Phillips, M. Simon, M. Harter, K. Costello, Y . Gaw- diak, and S. Paine, “Space trusted autonomy readiness levels,” in 2023 IEEE Aerospace Conference . IEEE, 2023, pp. 1–17

    2023

  61. [67]

    Historical sur- vey of kinematic and dynamic spacecraft simulators for laboratory experimentation of on-orbit proximity maneuvers,

    M. Wilde, C. Clark, and M. Romano, “Historical sur- vey of kinematic and dynamic spacecraft simulators for laboratory experimentation of on-orbit proximity maneuvers,” Progress in Aerospace Sciences, vol. 110, p. 100552, 2019

    2019

  62. [68]

    42: An open-source simulation tool for study and design of spacecraft attitude control systems,

    E. Stoneking, “42: An open-source simulation tool for study and design of spacecraft attitude control systems,” Tech. Rep., 2018

    2018

  63. [69]

    Basilisk: A flexible, scalable and modular astrodynamics simulation framework,

    P. W. Kenneally, S. Piggott, and H. Schaub, “Basilisk: A flexible, scalable and modular astrodynamics simulation framework,”Journal of Aerospace Information Systems, vol. 17, no. 9, pp. 496–507, 2023/05/25 2020

    2023

  64. [70]

    AGI Systems Tool Kit (STK) Website,

    “AGI Systems Tool Kit (STK) Website,” https://www. agi.com/products/stk, Accessed 2023

    2023

  65. [71]

    FreeFlyer Website,

    “FreeFlyer Website,” https://ai-solutions.com/products/ freeflyer/, Accessed 2023

    2023

  66. [72]

    General Mis- sion Analysis Tool (GMAT),

    NASA Goddard Space Flight Center, “General Mis- sion Analysis Tool (GMAT),” https://gmat.gsfc.nasa. gov, Accessed 2023

    2023

  67. [73]

    Introducing the lunar autonomous pnt sys- tem (laps) simulator,

    B. Hagenau, B. Peters, R. Burton, K. Hashemi, and N. Cramer, “Introducing the lunar autonomous pnt sys- tem (laps) simulator,” in 2021 IEEE Aerospace Confer- ence (50100), 2021, pp. 1–11

    2021

  68. [74]

    Development of a high-performance, heterogenous, scalable test-bed for distributed spacecraft,

    C. Adams, B. Kempa, W. Vaughan, and N. Cramer, “Development of a high-performance, heterogenous, scalable test-bed for distributed spacecraft,” in 2023 IEEE Aerospace Conference, 2023, pp. 1–8

    2023

  69. [75]

    J. M. Cameron, A. Jain, P. D. Burkhart, E. S. Bai- ley, B. Balaram, E. Bonfiglio, M. Ivanov, J. Benito, E. Sklyanskiy, and W. Strauss,DSENDS: Multi-mission Flight Dynamics Simulator for NASA Missions

  70. [76]

    Processor-in-the-loop simulations applied to the design and evaluation of a satellite attitude control,

    L. S. Martins-Filho, A. C. Santana, R. O. Duarte, and G. A. Junior, “Processor-in-the-loop simulations applied to the design and evaluation of a satellite attitude control,” in Computational and Numerical Simulations, J. Awrejcewicz, Ed. Rijeka: IntechOpen, 2014, ch. 8. [Onlin...

    2014 doi

  71. [77]

    Experimental characterization of inverse dy- namics guidance in docking with a rotating target,

    M. Wilde, M. Ciarci `a, A. Grompone, and M. Ro- mano, “Experimental characterization of inverse dy- namics guidance in docking with a rotating target,” Journal of Guidance, Control, and Dynamics , vol. 39, no. 6, pp. 1173–1187, 2016

    2016

  72. [78]

    Nrl engineers ready innovative robotic servicing of geosynchronous satellites (rsgs) payload for launch,

    U.S. Naval Research Laboratory, “Nrl engineers ready innovative robotic servicing of geosynchronous satellites (rsgs) payload for launch,” 14 https://www.nrl.navy.mil/Media/News/Article/3214111/nrl- engineers-ready-innovative-robotic-servicing-of- geosynchronous-satellites-r/,...

    2022

  73. [79]

    D.-M. Cho, D. Jung, and P. Tsiotras,A 5-dof Experimen- tal Platform for Spacecraft Rendezvous and Docking

  74. [80]

    Astros: a 5dof experimental platform for research in spacecraft proximity operations

    P. Tsiotras, “Astros: a 5dof experimental platform for research in spacecraft proximity operations.” Georgia Institute of Technology, 2014

    2014

  75. [81]

    Air-bearing- based satellite attitude dynamics simulator for con- trol software research and development,

    B. N. Agrawal and R. E. Rasmussen, “Air-bearing- based satellite attitude dynamics simulator for con- trol software research and development,” in Technolo- gies for Synthetic Environments: Hardware-in-the-Loop Testing VI, vol. 4366. SPIE, 2001, pp. 204–214

    2001

  76. [82]

    Historical review of air-bearing spacecraft simulators,

    J. L. Schwartz, M. A. Peck, and C. D. Hall, “Historical review of air-bearing spacecraft simulators,” Journal of Guidance, Control, and Dynamics , vol. 26, no. 4, pp. 513–522, 2003

    2003

  77. [83]

    Hardware-in-the-loop multi-satellite simulator for proximity operations,

    G. Gaias, S. D’Amico, J.-S. Ardeans, and T. Boge, “Hardware-in-the-loop multi-satellite simulator for proximity operations,” 2010

    2010

  78. [84]

    Nasa operational simulator for small satellites (nos3): Tools for software-based validation and verification of small satellites,

    M. Grubb, J. Morris, S. Zemerick, and J. Lucas, “Nasa operational simulator for small satellites (nos3): Tools for software-based validation and verification of small satellites,” 2016

    2016

  79. [85]

    Ter- restrial testing of multi-agent, relative guidance, navi- gation, and control algorithms,

    M. Mercier, S. Phillips, M. Shubert, and W. Dong, “Ter- restrial testing of multi-agent, relative guidance, navi- gation, and control algorithms,” in 2020 IEEE/ION Po- sition, Location and Navigation Symposium (PLANS) , 2020, pp. 1488–1497

    2020

  80. [86]

    R. M. McCarthy, T. Thomas, C. Danielson, S. Phillips, and R. Fierro, Control for an Omnidirectional Multi- rotor UAV for Space Applications

  81. [87]

    Phillips, Z

    S. Phillips, Z. Lippay, D. Baker, A. A. Soderlund, and M. Shubert, Emulation of Close-Proximity Spacecraft Dynamics in Terrestrial Environments Using Unmanned Aerial Vehicles

  82. [89]

    Testing spacecraft formation flying with crazyflie drones as satellite surrogates,

    A. de la Barcena, C. Rhodes, J. McCarroll, M. Cescon, and K. L. Hobbs, “Testing spacecraft formation flying with crazyflie drones as satellite surrogates,” in 2024 IEEE Aerospace Conference. IEEE, 2024, pp. 1–9

    2024

  83. [91]

    D.-M. Cho, D. Jung, and P. Tsiotras, A 5-dof Experimental Platform for Spacecraft Rendezvous and Docking. [Online]. Available: https://arc.aiaa.org/doi/ abs/10.2514/6.2009-1869 BIOGRAPHY [ Kerianne L. Hobbs is the Safe Au- tonomy and Space Lead on the Auton- omy Capability Tea...

    2009 doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.