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REVIEW 3 major objections 5 minor 68 references

TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read This paper claims that a single learned generative model can take any Signal Temporal Logic (STL) specification and an initial state and output a trajectory that satisfies it—the first general STL-conditioned planner.

desk verdict A useful STL-conditioned generative planner and dataset, but the 'general STL' claim and the maze data protocol need to be tightened before publication. read the letter →

arxiv 2505.00562 v1 pith:ACVKITZJ submitted 2025-05-01 cs.RO cs.AIcs.FLcs.LG

Yue Meng , Chuchu Fan This is my paper · ORCID
classification cs.ROcs.AIcs.FLcs.LG
keywords signaltemporallogicflowmatchinggraphneuralnetworktrajectoryplanningconditionalgenerativemodelrobotmotionSTLsatisfaction
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

TeLoGraF claims to be the first learned model that takes a general Signal Temporal Logic (STL) specification as input and produces trajectories that satisfy it, without retraining per formula. The paper identifies four common STL task patterns—single-goal, multi-goal, sequential, and partial-order—and builds a dataset of 200K specifications with paired demonstration trajectories across five simulated robot systems. The method encodes each STL formula as a graph and uses flow matching to generate trajectories conditioned on that graph embedding and the initial state. This matters because classical STL planners are slow and struggle in high dimensions, while earlier learned methods handled only fixed or parameterized formulas, so a working general model would be the first of its kind and 10 to 100 times faster at inference than classical planners.

What carries the argument

The load-bearing object is the STL syntax tree recast as a directed graph with child-to-parent edges, where each node holds an 8-dimensional feature vector (operator type; start and end times, with a default of -1; object coordinates and radius or side length; and a binary flag marking a left child of an Until operator). A multi-layer GNN with a permutation-invariant aggregation performs message passing over this graph and reads out a fixed-dimension embedding; that embedding, concatenated with the initial state, conditions a temporal U-Net trained with the flow-matching loss $\mathcal{L}_{\mathrm{FM}} = \mathbb{E}_{t,X_0,X_1}\|H_\omega(F_\theta(G), x_0, t, X_t) - (X_1 - X_0)\|^2$, where $X_t = tX_1 + (1-t)X_0$. Generation integrates the learned velocity field $\frac{d}{dt}\psi(t,X) = H_\omega(F_\theta(G), x_0, t, \psi(t,X))$ by Euler steps starting from Gaussian noise, and the ODE solution at $t=1$ is the planned trajectory. The expressiveness argument rests on the Weisfeiler–Leman test being complete for trees, which the paper uses to conclude that distinct STL syntax trees receive distinct embeddings.

What would settle it

Build a validation set of STL formulas whose syntax trees go deeper than the four templates—for example, nested $\Box_{[a,b]} \Diamond_{[c,d]}$ combinations over conjunctions of reach and avoid predicates—and measure the satisfaction rate of TeLoGraF's sampled trajectories in one benchmark environment. If the rate collapses to the level of the sequence-encoder baselines, the claim that graph encoding generalizes across STL structure is refuted; if satisfaction already drops when time windows are narrowed by a small margin below training values, the model's scope is narrower than the 'general STL' claim.

Watch

Extended reading notes

Core claim

The central claim is that an STL formula's syntax tree can be turned into a directed graph—each operator and atomic proposition becomes a node carrying features for operator type, time interval, object geometry, and Until-child role—and a graph neural network embedding of that graph suffices to condition a flow-matching trajectory generator, so that trajectories drawn from $p_\theta(\tau \mid x_0, \phi)$ satisfy the specification, $\tau, 0 \models \phi$. The authors justify the encoding by noting that the Weisfeiler–Leman graph test is complete for trees, so a GNN can in principle distinguish distinct STL syntax trees. Empirically, the GNN-conditioned model posts the highest STL satisfaction rates against guidance-based learning baselines and classical gradient and sampling planners across five environments, and with only ten ODE integration steps it retains most of its satisfaction while running over a hundred times faster than classical baselines in the Franka Panda environment. The paper further claims that graph encoding generalizes better than sequence encoders to unseen and synthetically augmented STL structures.

Load-bearing premise

The claim of generality rests on the four procedural STL templates (single-goal, multi-goal, sequential, partial) standing in for all practically useful STL specifications, so formulas outside these patterns, or with time constraints tighter than the demonstration distribution, may not be satisfiable by the learned model.

Editorial extensions

If this is right

  • A single trained model covers the four STL templates and formulas built from them, so a user can query new specifications at test time without retraining.
  • Because generation is a short ODE solve rather than an optimization loop, inference is fast enough for real-time use; the paper reports 10 to 100 times speedups and, with ten flow steps, roughly 123.6 times faster planning than the gradient baseline on the 7-DoF Franka Panda arm at a higher satisfaction rate.
  • The approach does not require differentiable dynamics, so it applies to maze navigation and other non-differentiable systems as long as demonstration trajectories can be collected.
  • Graph-based encoding propagates time intervals and temporal ordering better than sequence encoders, which is where the satisfaction gains over GRU and Transformer baselines concentrate, namely sequential and partial-order templates.

Reading between the lines

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

  • If the four templates capture most practical STL tasks, the bottleneck shifts to demonstration collection: the model is only as broad as the solver that produced its training trajectories, so better offline datasets could extend the same graph-conditioned generator to new environments.
  • The maze demonstrations were made by planning a path and then inferring time intervals that make it satisfy the formula, so a testable consequence is that the model learns comfortable time margins and satisfaction should degrade sharply when queried with time windows narrower than any seen in training.
  • The graph encoder is agnostic to the generative backbone and the operator vocabulary, so the same conditioning could steer diffusion or consistency samplers, and the operator set could be extended past reach and avoid to predicates over continuous signals.
  • Coupling TeLoGraF's fast proposals with a classical STL verifier as a filter would yield an anytime planner with soundness guarantees on accepted trajectories, addressing the paper's stated lack of guarantees.
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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. The paper proposes TeLoGraF, a conditional generative model for trajectory planning under Signal Temporal Logic (STL) specifications. STL formulas are encoded as syntax-tree graphs and processed by a GNN, and a flow-matching model generates trajectories conditioned on the STL embedding and initial state. The authors identify four STL templates (single-goal, multi-goal, sequential, partial), generate roughly 200K specifications with paired demonstration trajectories in five simulation environments (Linear, Dubins, PointMaze, AntMaze, Franka Panda), and compare TeLoGraF against classical planners (Grad, CEM) and learning-based baselines (CTG, LTLDoG). The paper claims state-of-the-art STL satisfaction rates, 10-100x faster inference than classical methods, and the ability to handle 'general STL specifications.'

Significance. If the central claims were fully supported, TeLoGraF would be a valuable contribution: it is among the first learned models to take STL syntax as input and generate trajectories, and the GNN encoding of STL syntax trees is a sensible and well-motivated design choice. The dataset of 200K STL-conditioned demonstrations, if released, could be a useful benchmark for the community. The paper also provides a theoretical argument for why GNNs can distinguish STL syntax trees and runs a systematic encoder comparison. However, the evidence falls short of the 'general STL' claim: the four templates are a narrow fragment of the STL grammar, the per-template satisfaction on complex templates is low by the paper's own figures, the best-of-1024 evaluation protocol without variance estimates is optimistic, and the maze demonstrations are generated by fitting time intervals to pre-planned trajectories rather than respecting fixed user-specified deadlines. The core methodology is defensible for the four templates, but the headline claims need substantial revision or additional evidence.

major comments (3)
  1. [Sec. 4.2, Eq. (3); Sec. 5.4, Fig. 7; Sec. 4.1] The paper's central claim that TeLoGraF handles 'general STL specifications' (Abstract; Sec. 4.1) is not supported by the presented grammar and results. Equation (3) covers only four templates whose atomic propositions are restricted to reach/avoid predicates, a small fragment of the STL grammar in Eq. (1) that includes arbitrary predicates, nested Until, and unbounded combinations. More importantly, the paper's own Fig. 7 shows that validation satisfaction for templates II-IV is 'relatively low' on Linear, Dubins, and Franka Panda; for Dubins, Sec. 5.2 reports a validation satisfaction rate of 0.45 for TeLoGraF even with best-of-1024 selection, and per-template rates for sequential and partial are lower. The expressiveness argument in Sec. 4.4 only shows that a GNN can distinguish different syntax trees, not that the generated trajectories satisfy the STL formula. The abstract and introduction should be revised to claim handling of 'four common templates' rather than 'general STL,' or the evaluation should include formulas outside these templates.
  2. [Appendix A.1.3] The maze data pipeline undermines the evaluation of time constraints. Appendix A.1.3 states that, after planning a trajectory, the authors 'randomly infer the possible time intervals for the goal-reaching sub-formulas and run post-verification to ensure the trajectory satisfies this STL.' This means the STL formulas in PointMaze and AntMaze are constructed to be feasible for already-generated trajectories, so the model is never tested on user-specified deadlines that are fixed in advance. Consequentially, the reported high satisfaction rates in maze environments do not measure the model's ability to satisfy exact temporal requirements. The experiments should either fix time intervals before planning or report maze results separately with this feasibility-by-construction caveat.
  3. [Sec. 5, Metrics] The evaluation protocol is optimistic and lacks statistical grounding. The paper states that for each STL, 1024 trajectories are sampled and the one with the highest STL score is selected as the final trajectory. Reporting best-of-1024 satisfaction without error bars, multiple seeds, or any statistical comparison makes the claimed superiority over baselines fragile; for instance, the differences between TeLoGraD, TeLoGraF, TreeLSTM, and the guidance-based baselines in Fig. 4 could be within sampling noise under this protocol. The authors should report mean and variance over multiple random seeds, report both mean and best-over-sample statistics, and describe how many STL specifications and seeds are used in each reported number.
minor comments (5)
  1. [Sec. 3.2] There is a typo: 'integrating the predicted vector field over over time' should read 'over time.'
  2. [Appendix A.1.2] The last sentence says 'The loss is similar to Eq. (8),' but Eq. (8) is the Dubins dynamics; the intended reference appears to be Eq. (7), the gradient-based loss.
  3. [Appendix C.4] The section heading reads 'AntMaze,' but Figures 31-37 are labeled 'PointMaze environment'; this mismatch should be corrected.
  4. [Eq. (3) and surrounding text] The explanation of phi_reach's stay condition says the robot stays for the time interval [ta + tc, tb + td], but the grammar only defines G[tc,td] after F[ta,tb]; the relationship between the two intervals should be clarified or corrected.
  5. [Sec. 5, Metrics and Fig. 6] Figure 6 reports training and validation satisfaction for different ODE steps without confidence intervals; adding error bars would make the claim that performance 'does not drop until 10 steps' more robust.

Circularity Check

1 steps flagged · score 5.0 of 10

Maze data pipeline fits STL time intervals to demonstrations, making the maze satisfaction results partially circular; otherwise the method is an empirical pipeline with no derivation to be circular.

  1. fitted input called prediction [Appendix A.1.3 (PointMaze/AntMaze data collection)]
    "we first generate the skeleton of the STL syntax but leave the time intervals as placeholders; then, we use a planning algorithm to find waypoints and then use a PD goal-reaching controller to collect trajectories. After the generation of the trajectories, we first ensure it reaches the final destination, and then we evaluate the robustness score on the trajectory without considering the time intervals. If the robustness score is positive, we randomly infer the possible time intervals for the goal-reaching sub-formulas and run post-verification to ensure the trajectory satisfies this STL."

    In PointMaze and AntMaze, the STL specification is not fixed before planning; its time intervals are inferred after a trajectory has been collected, so the paired STL is constructed from the output trajectory rather than given as an external task. Training and evaluating on these pairs means the model is asked to reproduce trajectories that already match STLs reverse-engineered to fit that very trajectory distribution. The high maze satisfaction rates therefore do not demonstrate ability to satisfy independently user-specified temporal constraints; they reflect that the temporal constraints were fitted to the demonstrations by construction.

full rationale

The paper is primarily an empirical learning pipeline, not a mathematical derivation, so most of the usual circular-derivation patterns do not apply. There is no load-bearing self-citation chain, no imported uniqueness theorem from the authors' prior work, and no ansatz smuggled in via citation: the GNN-expressiveness argument in Sec. 4.4 appeals to external results (Xu et al. 2018; Kiefer 2020), which are independent. The four STL templates in Eq. (3) are a narrow fragment of the full STL grammar of Eq. (1), and Sec. 5.4 candidly reports that all models perform 'relatively low' on templates II-IV on Linear, Dubins, and Franka Panda; Fig. 6 shows Dubins validation satisfaction of 0.45 even with best-of-1024 selection. These are limitations on the generality of the central claim, and the Limitations paragraph acknowledges degraded performance on complex or out-of-distribution STLs, but they are correctness/scope concerns, not circularity. The one genuinely circular element is the maze data-generation protocol in Appendix A.1.3: time intervals are inferred post hoc to make collected trajectories satisfy the STL, so the model's high satisfaction on PointMaze and AntMaze is partly an artifact of fitting the specification to the demonstration data. This is a fitted-input-called-prediction pattern affecting a substantial portion of the experimental evidence, but it does not make the entire method circular, since the Linear, Dubins, and Franka Panda benchmarks fix the STL before solving and the flow-matching training itself is not tautological. Score 5 reflects this partial, evaluation-level circularity rather than a derivation-level collapse.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The paper contributes a training pipeline and dataset, not a derivation, so the ledger mostly records modeling choices. The most consequential entries are the four hand-designed templates and the post hoc time-interval fitting in maze data collection; both shape what "general STL" means in the experiments. Standard STL semantics and WL-test theory are background.

free parameters (5)
  • Robustness margin threshold in Eq. (7) = 0.5
    Hand-chosen constant in the demonstration-generation loss; determines how strongly collected trajectories satisfy the STL.
  • Control regularization weights c1, c2 in Eq. (7) = not specified
    Hand-chosen weights balancing robustness against smoothness in the gradient-based data-collection solver.
  • Flow matching ODE steps Ns = 100
    Number of Euler steps for sampling; ablation (Sec. 5.2) shows satisfaction depends on it, with the fast variant using 10.
  • Number of sampled trajectories per STL at evaluation = 1024
    The reported metric selects the best-scoring trajectory from 1024 samples; this protocol drives the satisfaction numbers.
  • STL template and object-count distribution = 4 templates, 0-6 obstacles, 0-4 goals
    Dataset design choices defining the "general STL" the model sees; the claimed generality rests on this distribution.
assumptions (5)
  • standard math STL satisfaction and robustness semantics as defined in Donze and Maler (2010) and Donze et al. (2013)
    Used in Sec. 3.1 to evaluate formulas and compute the reported satisfaction metric.
  • domain assumption GNN expressiveness is bounded by the 1-WL test (Xu et al. 2018), and the WL test is complete for trees (Kiefer 2020), so GNNs can distinguish any two STL syntax trees
    Sec. 4.4; the implemented GCN's sum/mean pooling is not shown to be injective, so the theoretical guarantee may not hold exactly.
  • ad hoc to paper The four templates in Eq. (3) cover the practically relevant and "general" STL specifications
    Sec. 4.2; this is the paper's own taxonomy and directly bounds the generality claim.
  • domain assumption High-quality demonstrations can be produced by the chosen off-the-shelf solvers
    Sec. 5 and Appendix A; the learned model's ceiling is the quality of these demonstrations.
  • ad hoc to paper STL time intervals can be inferred from a planned trajectory to make the formula satisfiable (maze domains)
    Appendix A.1.3; this removes the challenge of arbitrary fixed deadlines in PointMaze and AntMaze.

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Cite this review

Pith. "Pith review of TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching." pith.science (2026). https://pith.science/paper/ACVKITZJ

@misc{pith2026250500562,
  author       = {Pith},
  title        = {Pith review of: TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ACVKITZJ}},
  note         = {Machine review of arXiv:2505.00562}
}
read the original abstract

Learning to solve complex tasks with signal temporal logic (STL) specifications is crucial to many real-world applications. However, most previous works only consider fixed or parametrized STL specifications due to the lack of a diverse STL dataset and encoders to effectively extract temporal logic information for downstream tasks. In this paper, we propose TeLoGraF, Temporal Logic Graph-encoded Flow, which utilizes Graph Neural Networks (GNN) encoder and flow-matching to learn solutions for general STL specifications. We identify four commonly used STL templates and collect a total of 200K specifications with paired demonstrations. We conduct extensive experiments in five simulation environments ranging from simple dynamical models in the 2D space to high-dimensional 7DoF Franka Panda robot arm and Ant quadruped navigation. Results show that our method outperforms other baselines in the STL satisfaction rate. Compared to classical STL planning algorithms, our approach is 10-100X faster in inference and can work on any system dynamics. Besides, we show our graph-encoding method's capability to solve complex STLs and robustness to out-distribution STL specifications. Code is available at https://github.com/mengyuest/TeLoGraF

Figures

Figures reproduced from arXiv: 2505.00562 by the authors.

Figure 1
Figure 1. Four STL templates used in this paper. Single-goal: reach one goal under time constraints while avoiding obstacles. Multi-goal: reach one of the valid subsets of the goals. Sequential: All the goals needed to be reached in a strict temporal order. Partial: Some goals must be reached first before reaching other goals (no global order is explicitly specified). properties via the following rules (Donze et al. ´ , 2013)… view at source ↗
Figure 2
Figure 2. System diagram for TeLoGraF. The STL specification is encoded as a graph structure data. The GNN extracted [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Simulation benchmarks. In Linear and Dubins, a moving robot needs to reach circular regions and avoid circular obstacles. In PointMaze and AntMaze, the agent needs to reach/avoid square tiles in a maze. In Franka Panda, the robot arm needs to reach certain cubes on the table while not colliding with red balls. here γ(.) and ψ are the nonlinear update and message func￾tions to increase the expressiveness of the GNN, … view at source ↗
Figures from the paper (46 more)
Figure 4
Figure 4. Figure 4: Main results. Our methods outperform both learning-based methods ( [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Different encoder architecture comparisons. GNN and TreeLSTM works the best on the validation set. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Ablation studies on ODE steps in Dubins environ [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: STL satisfaction rate distribution for different categories (I. single-goal; II. multi-goal; III. sequential; IV. partial). [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 8
Figure 8. Figure 8: Encoder robustness test under varied STL augmentation (shaded in red). Graph-based encoders (GNN, TreeLSTM) [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 9
Figure 9. Figure 9: Varied ODE steps, Dubins environment, TeLoGraF [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]
Figure 10
Figure 10. Figure 10: Varied ODE steps, Dubins environment, TeLoGraF [PITH_FULL_IMAGE:figures/full_fig_p014_10.png]
Figure 11
Figure 11. Figure 11: Varied ODE steps, Dubins environment, TeLoGraF [PITH_FULL_IMAGE:figures/full_fig_p014_11.png]
Figure 12
Figure 12. Figure 12: Varied ODE steps, Dubins environment, TeLoGraF [PITH_FULL_IMAGE:figures/full_fig_p015_12.png]
Figure 13
Figure 13. Figure 13: Linear environment. Single-goal STL [PITH_FULL_IMAGE:figures/full_fig_p015_13.png]
Figure 14
Figure 14. Figure 14: Linear environment. Single-goal STL [PITH_FULL_IMAGE:figures/full_fig_p015_14.png]
Figure 15
Figure 15. Figure 15: Linear environment. Multi-goal STL 15 [PITH_FULL_IMAGE:figures/full_fig_p015_15.png]
Figure 16
Figure 16. Figure 16: Linear environment. Multi-goal STL [PITH_FULL_IMAGE:figures/full_fig_p016_16.png]
Figure 17
Figure 17. Figure 17: Linear environment. Sequential STL [PITH_FULL_IMAGE:figures/full_fig_p016_17.png]
Figure 18
Figure 18. Figure 18: Linear environment. Sequential STL [PITH_FULL_IMAGE:figures/full_fig_p016_18.png]
Figure 19
Figure 19. Figure 19: Linear environment. Partial STL 16 [PITH_FULL_IMAGE:figures/full_fig_p016_19.png]
Figure 20
Figure 20. Figure 20: Linear environment. Partial STL C.2. Dubins environment In Dubin’s environment, the collected demonstration trajectories are more diverse and drastically change over time, owing to the large angular velocity and the acceleration rate [PITH_FULL_IMAGE:figures/full_fig…
Figure 21
Figure 21. Figure 21: Dubins environment. Single-goal STL [PITH_FULL_IMAGE:figures/full_fig_p017_21.png]
Figure 22
Figure 22. Figure 22: Dubins environment. Single-goal STL [PITH_FULL_IMAGE:figures/full_fig_p017_22.png]
Figure 23
Figure 23. Figure 23: Dubins environment. Multi-goal STL 17 [PITH_FULL_IMAGE:figures/full_fig_p017_23.png]
Figure 24
Figure 24. Figure 24: Dubins environment. Multi-goal STL [PITH_FULL_IMAGE:figures/full_fig_p018_24.png]
Figure 25
Figure 25. Figure 25: Dubins environment. Sequential STL [PITH_FULL_IMAGE:figures/full_fig_p018_25.png]
Figure 26
Figure 26. Figure 26: Dubins environment. Sequential STL [PITH_FULL_IMAGE:figures/full_fig_p018_26.png]
Figure 27
Figure 27. Figure 27: Dubins environment. Partial STL 18 [PITH_FULL_IMAGE:figures/full_fig_p018_27.png]
Figure 28
Figure 28. Figure 28: Dubins environment. Partial STL C.3. PointMaze Here, the pink trajectories are the demonstration data collected [PITH_FULL_IMAGE:figures/full_fig_p019_28.png]
Figure 29
Figure 29. Figure 29: PointMaze environment. Single-goal STL [PITH_FULL_IMAGE:figures/full_fig_p019_29.png]
Figure 30
Figure 30. Figure 30: PointMaze environment. Multi-goal STL C.4. AntMaze Here, the pink trajectories are the demonstration data collected. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_30.png]
Figure 31
Figure 31. Figure 31: PointMaze environment. Multi-goal STL [PITH_FULL_IMAGE:figures/full_fig_p020_31.png]
Figure 32
Figure 32. Figure 32: PointMaze environment. Sequential STL [PITH_FULL_IMAGE:figures/full_fig_p020_32.png]
Figure 33
Figure 33. Figure 33: PointMaze environment. Sequential STL 20 [PITH_FULL_IMAGE:figures/full_fig_p020_33.png]
Figure 34
Figure 34. Figure 34: PointMaze environment. Sequential STL [PITH_FULL_IMAGE:figures/full_fig_p021_34.png]
Figure 35
Figure 35. Figure 35: PointMaze environment. Sequential STL [PITH_FULL_IMAGE:figures/full_fig_p021_35.png]
Figure 36
Figure 36. Figure 36: PointMaze environment. Partial STL 21 [PITH_FULL_IMAGE:figures/full_fig_p021_36.png]
Figure 37
Figure 37. Figure 37: PointMaze environment. Partial STL [PITH_FULL_IMAGE:figures/full_fig_p022_37.png]
Figure 38
Figure 38. Figure 38: AntMaze environment. Single-goal STL [PITH_FULL_IMAGE:figures/full_fig_p022_38.png]
Figure 39
Figure 39. Figure 39: AntMaze environment. Single-goal STL 22 [PITH_FULL_IMAGE:figures/full_fig_p022_39.png]
Figure 40
Figure 40. Figure 40: AntMaze environment. Multi-goal STL [PITH_FULL_IMAGE:figures/full_fig_p023_40.png]
Figure 41
Figure 41. Figure 41: AntMaze environment. Multi-goal STL [PITH_FULL_IMAGE:figures/full_fig_p023_41.png]
Figure 42
Figure 42. Figure 42: AntMaze environment. Sequential STL 23 [PITH_FULL_IMAGE:figures/full_fig_p023_42.png]
Figure 43
Figure 43. Figure 43: AntMaze environment. Sequential STL [PITH_FULL_IMAGE:figures/full_fig_p024_43.png]
Figure 44
Figure 44. Figure 44: AntMaze environment. Partial STL [PITH_FULL_IMAGE:figures/full_fig_p024_44.png]
Figure 45
Figure 45. Figure 45: AntMaze environment. Partial STL 24 [PITH_FULL_IMAGE:figures/full_fig_p024_45.png]
Figure 46
Figure 46. Figure 46: Franka Panda environment. Single-goal STL [PITH_FULL_IMAGE:figures/full_fig_p025_46.png]
Figure 47
Figure 47. Figure 47: Franka Panda environment. Multi-goal STL [PITH_FULL_IMAGE:figures/full_fig_p025_47.png]
Figure 48
Figure 48. Figure 48: Franka Panda environment. Sequential STL [PITH_FULL_IMAGE:figures/full_fig_p026_48.png]
Figure 49
Figure 49. Figure 49: Franka Panda environment. Partial STL 26 [PITH_FULL_IMAGE:figures/full_fig_p026_49.png]

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.