Pith. sign in

REVIEW 5 major objections 5 minor 31 references

The paper claims that a risk-aware belief control barrier function built directly on SMC-PHD particles renders a robot safe among an unknown, time-varying number of objects, with forward invariance under continuous prediction and an explici

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

T0 review · deepseek-v4-flash

2026-08-02 00:24 UTC pith:BPLPNI6Y

load-bearing objection A novel BCBF for SMC-PHD beliefs with a genuinely clever tie-handling construction, but the global 'all t' guarantee advertised in Problem 1 is not proven — the hybrid chaining between prediction and update is the weak link. the 5 major comments →

arxiv 2607.15016 v1 pith:BPLPNI6Y submitted 2026-07-16 cs.RO

Risk-Aware Belief Control Barrier Functions over Random Finite Sets

Shaohang Han , Gang Chen , Yixi Cai , Ignacio Torroba , Ivan Stenius , Patric Jensfelt , Javier Alonso-Mora , Jana Tumova This is my paper
classification cs.RO
keywords belief control barrier functionrandom finite setSMC-PHD filterPoisson point processchance constraintforward invariancerisk-aware controlmulti-object tracking
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper tries to establish that safety for a robot moving among an unknown, time-varying number of objects can be certified directly on the particle belief produced by an SMC-PHD filter, without first clustering particles into individual object estimates. Using the Poisson point process structure of the filter, it converts the chance constraint 'no object in the failure set' into a bound on the expected number of failed particles, then replaces the discontinuous count with a locally Lipschitz soft-min barrier function. The authors prove forward invariance of the resulting safe set during continuous prediction and give an explicit condition under which discrete measurement updates preserve safety. If correct, this extends control barrier function guarantees to multi-object settings with unknown cardinality and ambiguous data association, while staying within real-time computational budgets.

Core claim

The paper's central claim is that the chance constraint Pr(h_o(x,o^(i)) ≥ 0 for all objects) ≥ 1−τ can be enforced in belief space using only the weighted particles of an SMC-PHD filter. Because the filter approximates the multi-object belief as a Poisson point process, the probability that no object lies in the failure set equals the void probability exp(−Λ(F(x),b)). The paper converts this into the requirement that the expected number of particles in the failure set be at most ln(1/(1−τ))/w, which is equivalent to keeping at least L−k_τ particles safe. It then replaces the discontinuous count with the locally Lipschitz soft-min h_b = −(1/κ) ln Σ_{i∈I*} e^{−κ s_i} over the top L−k_τ margins

What carries the argument

The load-bearing object is the soft-min belief barrier h_b = −(1/κ) ln Σ_{i∈I*} e^{−κ s_i} over the index set I* of the L−k_τ particles with largest safety margins s_i = h_o(x, ilde{o}^{(i)}). It converts a discontinuous indicator constraint into a locally Lipschitz function. Its nonsmoothness at ties is handled by an active-set decomposition into strictly-above particles H and tied particles T, where tied particles share a common coefficient ar{c}, yielding the tractable sufficient condition (20) whose size scales linearly in |T| instead of exponentially.

Load-bearing premise

The argument depends on the unenforced assumption that after each measurement update the particle belief returns to the tightened safe set before the next prediction interval starts; if it does not, the per-interval safety guarantees do not chain into the claimed all-times guarantee.

What would settle it

Run the proposed QP in a simulation where each discrete update adds exactly the maximum allowed weight (bound (21)) to particles inside the failure set, starting from the tightened safe set, and check whether the tightened barrier value h_b^{τ−ε} returns to nonnegative before the next update; if it stays negative on some interval while measurements arrive at the rated rate, the all-t≥0 claim fails.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Robots can run CBF-style safety filters using raw particle beliefs from an SMC-PHD filter, removing the need to cluster particles into per-object estimates.
  • The safety guarantee is risk-aware: the user chooses τ, and safety holds with probability at least 1−τ under the PHD belief during each prediction interval.
  • Discrete measurement updates can be certified by checking the explicit weight-increase bound (21), allowing safety to be audited at update times.
  • The QP structure scales linearly in the number of tied particles and is parallelizable, keeping average control computation below 5 ms on laptop hardware in the paper's experiments.
  • The method remains effective under false alarms and in dynamic, unstructured environments, as shown in simulations and underwater hardware tests.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: Taken at face value, the all-times guarantee in Problem 1 is not fully established, because the recovery from the original safe set back to the tightened safe set between updates is not enforced (Remark 4); a fair reading is safety on each continuous interval plus a one-step update condition.
  • Editorial inference: The void-probability transformation is general enough to apply to other random-finite-set filters that admit a tractable void probability, potentially yielding analogous belief barrier functions for cardinalized or multi-Bernoulli filters.
  • Editorial inference: The tie-handling trick of grouping equal-margin particles with a shared coefficient is a general construction that any soft-min CBF over a finite set of candidate functions could use to avoid enumerating exponentially many active selections.
  • Editorial inference: The paper leaves the mismatch between the PHD/PPP approximation and the true multi-object posterior unquantified; a distributionally robust variant that treats the PHD as an uncertain intensity would turn the risk guarantee into one that holds over a set of nearby beliefs.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 5 minor

Summary. This paper proposes a risk-aware belief control barrier function (BCBF) framework for safe robot control when the environment contains an unknown, time-varying number of moving objects. The multi-object state is represented by an RFS and estimated by an SMC-PHD filter whose particle set forms the belief state. The authors use the PPP approximation of the PHD to express the chance constraint (9) as a bound on the expected number of particles in the failure set, reformulate it as a max-min condition, and replace the inner minimum by the soft minimum (15) to obtain a locally Lipschitz BCBF h_b. They derive a nonsmooth CBF condition (20) that renders C_b forward invariant during continuous prediction (Theorem 1), and a condition (21) under which a discrete PHD update from a tightened safe set lands in the original safe set (Proposition 1, κ→∞). The controller is formulated as a QP. Experiments in FOV-maintenance and obstacle-avoidance scenarios, including a BlueROV hardware test, report low collision rates and average QP times below 5 ms. The paper claims that this solves Problem 1 for all t≥0.

Significance. If the stated guarantees were fully established, this would be a substantive contribution: it extends belief-space CBFs from fixed-cardinality filters to RFS/PHD beliefs, gives an explicit risk level τ, handles ties in the particle ranking with a tractable nonsmooth active-set argument, and provides an efficient parallel implementation with hardware validation. The continuous-prediction invariance argument is credible, and the reduction to |T|+1 constraints is useful. However, the headline guarantee is not proven. The proof does not chain across discrete updates, Proposition 1 holds only in the κ→∞ limit while the implementation uses κ=100, condition (21) is not checked, and the slack relaxation used in the QP invalidates the CBF condition when active. These are load-bearing gaps, so the paper cannot currently be accepted as providing the claimed 'for all t≥0' risk-aware safety certificate.

major comments (5)
  1. [§VI-C (Prop. 1, Remark 4)] The global statement of Problem 1 is not established. Theorem 1 guarantees forward invariance of C^{τ−ε}_b only during continuous prediction and only for trajectories that start in that set. Proposition 1 shows only that an update from C^{τ−ε}_b satisfying (21) lands in C^τ_b, and only as κ→∞. Remark 4 explicitly says that returning to C^{τ−ε}_b before the next update is not enforced. Thus after any update the next prediction interval may begin outside the set to which Theorem 1 applies; the induction across updates breaks. The paper delivers safety 'between updates', not the 'for all t≥0' guarantee stated in Problem 1. Please either add an enforced recovery mechanism or a time-varying CBF that keeps the state in the tightened set, or restate the contribution as a conditional per-interval guarantee.
  2. [§VI-C and §VII-C.1] Proposition 1 is proved only in the κ→∞ limit, but the experiments use κ=100. No finite-κ error bound is provided. Because h_b is a soft minimum over L−kτ particles, a strictly positive minimum of the selected s_i can still give h_b<0 when κ is finite and the number of near-tie particles is large; hence (21) does not imply (x,b+)∈C^τ_b for implemented parameters. The authors should either state the finite-κ correction, choose κ adaptively with a guaranteed threshold, or weaken the theorem to an approximate guarantee. The negative h^{τ−ε}_b values reported in Fig. 3 and 4 illustrate this gap rather than validating it.
  3. [§VI-C, Eq. (21)] Condition (21) is never checked or enforced in the implementations. The QP in §VI-D does not include it, and the experimental sections do not report Λ(F,b±) or verify the inequality. Consequently, Proposition 1 is not actually exercised in the experiments; the reported simulations and hardware runs provide empirical evidence only, not evidence that the certified condition holds. If the all-time safety claim is retained, (21) should be verified online, or a bound on the discrete update should be enforced.
  4. [§VII-A] The controller implemented in the experiments is not the certified controller. The CBF constraint (20) is relaxed with a slack variable to guarantee feasibility, and on hardware the relaxation is 'always active'. Whenever the relaxed constraint is active, condition (20) is violated and Theorem 1 does not apply. To bridge theory and implementation, the paper should report the frequency and magnitude of active slack, use a relaxation that preserves a formal safety margin, or clearly characterize the experiments as heuristic.
  5. [§IV and §VIII] The chance constraint in Problem 1 is formulated over the true object RFS O, but the derivation in §V relies on the PHD/PPP approximation of the posterior. The paper itself states that accounting for the mismatch between the estimated PPP belief and the true multi-object posterior is left for future work. Without a bound relating the true posterior to the PHD belief, satisfying (10)-(12) does not imply that (9) holds for the actual environment. This is an assumption gap rather than an error internal to the framework, but it should be stated as an explicit hypothesis of Problem 1 or removed from the claimed guarantee.
minor comments (5)
  1. [Table II] The column t_n is not defined; presumably it is navigation time. Please define it in the caption or text.
  2. [§III-B, Eq. (6)] After resampling the particles are said to share uniform weight, but the normalization of the total weight is not stated. Clarify whether w denotes the common post-resampling weight or the total weight divided by L.
  3. [§V-A, Eq. (11)] The approximation in (11) should be flagged as relying on the PHD/PPP assumption at the point of use. Remark 1 is helpful, but the posterior-mismatch caveat should appear here as well.
  4. [Fig. 3 and Fig. 4] Please define h^L_b and h^R_b in the captions and explain why min{h^L_b, h^R_b} is plotted. The text attributes spikes to discrete updates but does not relate them to condition (21).
  5. [§VII-C.1] 'Both methods use κ=100' — the baseline [30] may use a different soft-minimum objective. Clarify whether κ plays the same role in both formulations.

Circularity Check

0 steps flagged

No circularity: derivation is self-contained; limitations in Remark 4 and the conclusion are proof/assumption gaps, not circular reductions.

full rationale

The paper's derivation chain does not reduce any predicted quantity to a fitted input or to a self-citation. The BCBF h_b in (15) is deliberately defined so that its superlevel set encodes the risk constraint (12)-(14), and the forward-invariance argument (Theorem 1, via conditions (20) and the external nonsmooth CBF theorem [3]) supplies the nontrivial content: it shows the controller keeps the belief inside the set under (16). No parameter is fitted to data; tau, epsilon, kappa, gamma are user-specified. Proposition 1 is an algebraic sufficient condition: (21) plus the pre-update bound in C_b^{tau-epsilon} implies the post-update bound defining C_b^tau; this is a certificate, not a fitted prediction. The self-citations [5], [7] concern prior BCBF frameworks and are not load-bearing; no uniqueness theorem or ansatz is imported from the authors' earlier work. Two limitations are stated in the manuscript: Remark 4 admits that returning to C_b^{tau-epsilon} before each discrete update is 'not explicitly enforced by the current controller,' so the global 'for all t>=0' guarantee of Problem 1 is not proven; and the conclusion defers 'the mismatch between the estimated PPP belief and the true multi-object posterior.' These are correctness/assumption gaps rather than circularity and do not raise the circularity score.

Axiom & Free-Parameter Ledger

5 free parameters · 6 axioms · 0 invented entities

All load-bearing assumptions are standard robotics/estimation assumptions except the discrete-recovery condition, which the authors explicitly flag as unproven. The core construction is parameter-light: τ is a user-specified risk budget, κ and γ are hand-tuned CBF parameters, and ε is an ad hoc margin used only to make update-time safety plausible. No novel physical entities are introduced.

free parameters (5)
  • risk level τ = 0.05 in main sim/hardware; 0.02 and 0.2 in Table I
    User-specified probability bound in Problem 1; safety threshold ln(1/(1-τ)) depends on it.
  • tightening margin ε = e.g., 0.04 for τ=0.05 in Fig. 3
    Introduced in Sec. VI-C to create safety margin for discrete updates; no rule given for choosing it; the guaranteed condition (21) and recovery depend on it.
  • soft-minimum inverse temperature κ = 100 (both methods, Sec. VII-B/C)
    Finite-κ BCBF h_b under-approximates the hard min; Prop 1 guarantee only as κ→∞, so finite-κ safety margin is heuristic.
  • CBF gain γ = not stated in text
    Controls how quickly h_b may decrease; chosen by hand; affects conservativeness.
  • slack relaxation penalty/weight = not specified
    CBF constraint relaxed with heavily penalized slack; always active on hardware, so safety certificate can be violated when QP infeasible.
axioms (6)
  • domain assumption The SMC-PHD multi-object posterior is a PPP with intensity equal to the PHD (Sec. III-B, Remark 1).
    Used to convert the chance constraint into exp(-Λ(F))≥1-τ in Eq. (10); PPP approximation error is unmodeled and deferred in Sec. VIII.
  • domain assumption Object dynamics ξ and measurement model ℓ are known and identical for all objects; robot state x is known (Sec. III-IV).
    PHD prediction step and the CBF condition (20) both use these models; any model mismatch breaks the certificate.
  • domain assumption After resampling, particle weights are uniform and the PHD is faithfully approximated by L weighted particles (Sec. III-B, Eq. (6)).
    h_b and kτ are defined from this particle representation; finite-L approximation error is not part of the guarantee.
  • standard math The nonsmooth CBF theory of Glotfelter et al. [3] applies to h_b, including the Clarke-gradient active-set characterization at ties (Theorem 1).
    The forward-invariance proof for continuous prediction uses [3, Theorem 3].
  • ad hoc to paper The discrete update satisfies Λ(F,b+)-Λ(F,b-)≤ln(1+ε/(1-τ)) and the belief returns to C^{τ-ε}_b before the next update (Sec. VI-C, Prop. 1, Remark 4).
    The paper states the recovery property is not enforced; without it, the all-t guarantee in Problem 1 does not follow.
  • standard math h_o is locally Lipschitz, so sorting and soft-min preserve local Lipschitzness of h_b (Lemma 1).
    Needed for Clarke generalized gradient and nonsmooth CBF conditions.

pith-pipeline@v1.3.0-alltime-deepseek · 12958 in / 14541 out tokens · 158136 ms · 2026-08-02T00:24:46.505946+00:00 · methodology

0 comments
read the original abstract

Ensuring robot safety in unknown, dynamic environments is a fundamental requirement. It involves inferring the states of an unknown and time-varying number of moving objects from noisy, incomplete measurements. We address safe control under the induced multi-object state uncertainty with a risk-aware belief control barrier function (BCBF) framework. The uncertainty is captured by a random finite set (RFS) belief, estimated by a sequential Monte Carlo probability hypothesis density (SMC-PHD) filter that represents it with a set of particles. Building directly on these particles, we construct a nonsmooth BCBF, establish forward invariance of the safe set under continuous prediction, and derive an explicit condition under which discrete updates preserve safety. Simulation and real-world underwater experiments demonstrate the effectiveness and efficiency of the proposed approach.

Figures

Figures reproduced from arXiv: 2607.15016 by Gang Chen, Ignacio Torroba, Ivan Stenius, Jana Tumova, Javier Alonso-Mora, Patric Jensfelt, Shaohang Han, Yixi Cai.

Figure 1
Figure 1. Figure 1: Real-world experiment within a test tank. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: An illustration of the tied scenario. The three tied particles (red [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Multi-object FOV maintenance. Left: Snapshots at t = 0.1 s and 9.5 s, showing the robot (yellow dot) with its trajectory (black) and its FOV (pink sector), the true objects (blue dots), the particles (red dots), and the detections (white crosses). A black arrow indicates the objects’ moving direction. Right: Evolution of h gt o and min{h L b , hR b } with different risk levels over the simulation. TABLE I:… view at source ↗
Figure 5
Figure 5. Figure 5: Illustration of the obstacle avoidance settings in simulation, showing [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Snapshots of one simulation run of case C at [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Snapshots of one hardware run in the head-on collision scenario. [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

31 extracted references · 1 linked inside Pith

  1. [1]

    Safety-critical control for autonomous systems: Control barrier functions via reduced-order models,

    M. H. Cohen, T. G. Molnar, and A. D. Ames, “Safety-critical control for autonomous systems: Control barrier functions via reduced-order models,”Annual Reviews in Control, vol. 57, p. 100947, 2024

    2024

  2. [2]

    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,” in2019 18th European control conference (ECC). IEEE, 2019, pp. 3420–3431

    2019

  3. [3]

    Nonsmooth barrier func- tions with applications to multi-robot systems,

    P. Glotfelter, J. Cort ´es, and M. Egerstedt, “Nonsmooth barrier func- tions with applications to multi-robot systems,”IEEE control systems letters, vol. 1, no. 2, pp. 310–315, 2017

    2017

  4. [4]

    Thrun, W

    S. Thrun, W. Burgard, and D. Fox,Probabilistic Robotics, ser. Intel- ligent Robotics and Autonomous Agents. Cambridge, Massachusetts London: MIT Press, 2006

    2006

  5. [5]

    Belief control barrier functions for risk-aware control,

    M. Vahs, C. Pek, and J. Tumova, “Belief control barrier functions for risk-aware control,”IEEE Robotics and Automation Letters, vol. 8, no. 12, pp. 8565–8572, 2023

    2023

  6. [6]

    Risk-aware control for robots with non- gaussian belief spaces,

    M. Vahs and J. Tumova, “Risk-aware control for robots with non- gaussian belief spaces,” in2024 IEEE International Conference on Robotics and Automation (ICRA), 2024, pp. 11 661–11 667

    2024

  7. [7]

    Risk-aware robot control in dynamic environments using belief control barrier functions,

    S. Han, M. Vahs, and J. Tumova, “Risk-aware robot control in dynamic environments using belief control barrier functions,” in2025 IEEE 64th Conference on Decision and Control (CDC), 2025, pp. 5973– 5979

    2025

  8. [8]

    Multitarget bayes filtering via first-order multitarget moments,

    R. P. Mahler, “Multitarget bayes filtering via first-order multitarget moments,”IEEE Transactions on Aerospace and Electronic systems, vol. 39, no. 4, pp. 1152–1178, 2004

    2004

  9. [9]

    Sequential monte carlo methods for multitarget filtering with random finite sets,

    B.-N. V o, “Sequential monte carlo methods for multitarget filtering with random finite sets,”IEEE Transactions on Aerospace and elec- tronic systems, vol. 41, no. 4, pp. 1224–1245, 2005

    2005

  10. [10]

    Improved smc implementation of the phd filter,

    B. Ristic, D. Clark, and B.-N. V o, “Improved smc implementation of the phd filter,” in2010 13th International Conference on Information Fusion. IEEE, 2010, pp. 1–8

    2010

  11. [11]

    Continuous occupancy mapping in dynamic environments using particles,

    G. Chen, W. Dong, P. Peng, J. Alonso-Mora, and X. Zhu, “Continuous occupancy mapping in dynamic environments using particles,”IEEE Transactions on Robotics, vol. 40, pp. 64–84, 2024

    2024

  12. [12]

    Sensor control for multi-object state-space estimation using random finite sets,

    B. Ristic and B.-N. V o, “Sensor control for multi-object state-space estimation using random finite sets,”Automatica, vol. 46, no. 11, pp. 1812–1818, 2010

    2010

  13. [13]

    Detecting, localizing, and tracking an unknown number of moving targets using a team of mobile robots,

    P. Dames, P. Tokekar, and V . Kumar, “Detecting, localizing, and tracking an unknown number of moving targets using a team of mobile robots,”The International Journal of Robotics Research, vol. 36, no. 13-14, pp. 1540–1553, 2017

    2017

  14. [14]

    Sensor-based distributionally robust control for safe robot navigation in dynamic environments,

    K. Long, Y . Yi, Z. Dai, S. Herbert, J. Cort ´es, and N. Atanasov, “Sensor-based distributionally robust control for safe robot navigation in dynamic environments,”The International Journal of Robotics Research, vol. 45, no. 2, pp. 328–351, 2026

    2026

  15. [15]

    Robust safe learning and control in an unknown environment: An uncertainty-separated control barrier function approach,

    J. Li, Q. Liu, W. Jin, J. Qin, and S. Hirche, “Robust safe learning and control in an unknown environment: An uncertainty-separated control barrier function approach,”IEEE Robotics and Automation Letters, vol. 8, no. 10, pp. 6539–6546, 2023

    2023

  16. [16]

    Observer-based control barrier functions for safety critical systems,

    Y . Wang and X. Xu, “Observer-based control barrier functions for safety critical systems,” in2022 American Control Conference (ACC). IEEE, 2022, pp. 709–714

    2022

  17. [17]

    Guaranteeing safety of learned perception modules via measurement-robust control barrier functions,

    S. Dean, A. Taylor, R. Cosner, B. Recht, and A. Ames, “Guaranteeing safety of learned perception modules via measurement-robust control barrier functions,” inConference on Robot Learning. PMLR, 2021, pp. 654–670

    2021

  18. [18]

    Risk-aware robotics: Tail risk measures in planning, control, and verification [focus on education],

    P. Akella, A. Dixit, M. Ahmadi, L. Lindemann, M. P. Chapman, G. J. Pappas, A. D. Ames, and J. W. Burdick, “Risk-aware robotics: Tail risk measures in planning, control, and verification [focus on education],” IEEE Control Systems, vol. 45, no. 4, pp. 46–78, 2025

    2025

  19. [19]

    Localization from semantic observations via the matrix permanent,

    N. Atanasov, M. Zhu, K. Daniilidis, and G. J. Pappas, “Localization from semantic observations via the matrix permanent,”The Interna- tional Journal of Robotics Research, vol. 35, no. 1-3, pp. 73–99, 2016

    2016

  20. [20]

    Random-finite-set-based dis- tributed multirobot slam,

    L. Gao, G. Battistelli, and L. Chisci, “Random-finite-set-based dis- tributed multirobot slam,”IEEE Transactions on Robotics, vol. 36, no. 6, pp. 1758–1777, 2020

    2020

  21. [21]

    Last and M

    G. Last and M. Penrose,Lectures on the Poisson process. Cambridge University Press, 2018, vol. 7

    2018

  22. [22]

    Composing control barrier functions for complex safety specifications,

    T. G. Molnar and A. D. Ames, “Composing control barrier functions for complex safety specifications,”IEEE Control Systems Letters, vol. 7, pp. 3615–3620, 2023

    2023

  23. [23]

    Sorting out lipschitz function approximation,

    C. Anil, J. Lucas, and R. Grosse, “Sorting out lipschitz function approximation,” inInternational conference on machine learning. PMLR, 2019, pp. 291–301

    2019

  24. [24]

    Control barrier functions for signal temporal logic tasks,

    L. Lindemann and D. V . Dimarogonas, “Control barrier functions for signal temporal logic tasks,”IEEE control systems letters, vol. 3, no. 1, pp. 96–101, 2018

    2018

  25. [25]

    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- Milneet al., “Jax: composable transformations of python+ numpy programs,” 2018

    2018

  26. [26]

    Osqp: An operator splitting solver for quadratic programs,

    B. Stellato, G. Banjac, P. Goulart, A. Bemporad, and S. Boyd, “Osqp: An operator splitting solver for quadratic programs,” in2018 UKACC 12th international conference on control (CONTROL). IEEE, 2018, pp. 339–339

    2018

  27. [27]

    Distributed control of a limited angular field-of-view multi-robot system in communication-denied scenarios: A probabilistic approach,

    M. Catellani and L. Sabattini, “Distributed control of a limited angular field-of-view multi-robot system in communication-denied scenarios: A probabilistic approach,”IEEE Robotics and Automation Letters, vol. 9, no. 1, pp. 739–746, 2023

    2023

  28. [28]

    On adaptive underwater object detection,

    D. P. Williams, “On adaptive underwater object detection,” in2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2011, pp. 4741–4748

    2011

  29. [29]

    Pybullet, a python module for physics sim- ulation for games, robotics and machine learning,

    E. Coumans and Y . Bai, “Pybullet, a python module for physics sim- ulation for games, robotics and machine learning,” http://pybullet.org, 2016–2021

    2016

  30. [30]

    Safe quadrotor navigation using composite control barrier functions,

    M. Harms, M. Jacquet, and K. Alexis, “Safe quadrotor navigation using composite control barrier functions,” in2025 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2025, pp. 6343–6349

    2025

  31. [31]

    Marinarium: A modular experimental facility for repro- ducible maritime and space-analog field robotics,

    I. Torroba, D. Dorner, V . N. Fernandez-Ayala, M. Kartasev, J. Ver- hagen, E. Krantz, G. Marchesini, C. Ljung, P. Roque, C. Sidrane et al., “Marinarium: A modular experimental facility for repro- ducible maritime and space-analog field robotics,”arXiv preprint arXiv:2602.23053, 2026

    Pith/arXiv arXiv 2026