arxiv: 2605.01222 · v1 · submitted 2026-05-02 · 💻 cs.AI

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Zero-Shot Signal Temporal Logic Planning with Disjunctive Branch Selection in Dynamic Semantic Maps

Bowen Ye , Ancheng Hou , Junyue Huang , Ruijia Liu , Xiang Yin

Authors on Pith no claims yet

Pith reviewed 2026-05-09 15:03 UTC · model grok-4.3

classification 💻 cs.AI

keywords zero-shot planningSignal Temporal Logicdynamic semantic mapsdisjunctive branch selectionTransformer architectureTransitive Reinforcement Learningtrajectory generationsafety-critical control

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The pith

A map-conditioned Transformer with heuristic branch selection enables zero-shot STL planning in changing environments.

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

The paper presents a planning solver for Signal Temporal Logic specifications that produces feasible trajectories in environments whose maps and obstacles change, all without retraining the system for each new layout. It uses a Transformer that receives map information together with a simple heuristic to choose among disjunctive subformulas, plus Transitive Reinforcement Learning to keep timing and logical consistency when the original formula is broken into subtasks. This matters because STL supplies machine-checkable guarantees needed for safety-critical control, yet prior exact methods are too slow and learning methods usually require retraining when the scene changes. If the approach works, agents could follow complex verifiable instructions across unpredictable real-world spaces. Experiments on dynamic semantic maps with varied obstacle placements report consistent performance improvements and wider coverage of STL formulas.

Core claim

The central claim is that a map-conditioned Transformer architecture combined with a lightweight heuristic for disjunctive branch selection and Transitive Reinforcement Learning produces feasible trajectories for complex STL specifications in variable-map environments without retraining or map-specific tuning.

What carries the argument

The map-conditioned Transformer architecture paired with a lightweight heuristic that selects branches among disjunctive subformulas, together with Transitive Reinforcement Learning that enforces consistent temporal grounding across the resulting sub-tasks.

If this is right

The solver generates feasible trajectories for STL tasks in environments whose maps change after training.
Complex disjunctive subformulas are handled without requiring the full formula to be solved as a single optimization problem.
Temporal grounding and logical coherence are maintained across decomposed sub-tasks by the Transitive Reinforcement Learning component.
Performance gains appear consistently across diverse obstacle layouts in dynamic semantic maps.
The framework covers a broader range of STL specifications than prior zero-shot methods.

Where Pith is reading between the lines

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

The same decomposition strategy might apply to other temporal-logic planning settings where formulas contain many choice points.
If the heuristic scales, it could reduce reliance on large environment-specific datasets for training robotic planners.
Real-time map updates from sensors could be fed directly into the Transformer without retraining the policy.
The approach suggests a route toward STL planners that remain safe even when the surrounding geometry is only partially known in advance.

Load-bearing premise

The lightweight heuristic together with Transitive Reinforcement Learning can reliably decompose and ground complex disjunctive STL formulas across previously unseen map layouts without retraining or map-specific tuning.

What would settle it

Observing that the method produces no valid trajectory for a disjunctive STL formula on a new obstacle layout that blocks one branch but leaves another feasible would falsify the claim of reliable zero-shot generalization.

Figures

Figures reproduced from arXiv: 2605.01222 by Ancheng Hou, Bowen Ye, Junyue Huang, Ruijia Liu, Xiang Yin.

**Figure 1.** Figure 1: Overview of the STL-solving pipeline (left) and training of the autoregressive Transformer (right). view at source ↗

**Figure 2.** Figure 2: Qualitative rollouts across dynamics and map scales. Top row: unicycle (UNI). Bottom row: double-integrator (DI). For each subfigure, the STL specification after heuristic selection is shown at the top of the plot. All experiments are conducted on a workstation with an NVIDIA A800 GPU (80GB) and an AMD EPYC 7542 CPU (2 sockets, 32 cores per socket, 128 logical CPUs). Training uses a single GPU, while evalu… view at source ↗

**Figure 3.** Figure 3: Effect of disjunctive branch selection under DI dynamics. Each plot shows the STL subformula after branch selection. Here, the disjunction is satisfied by reaching any one of three target regions. The heuristic preferentially selects a simpler, more feasible branch, whereas random selection may choose a harder branch and yield infeasible rollouts. trajectories across three map scales (U/M/L), highlighting … view at source ↗

read the original abstract

Signal Temporal Logic (STL) offers verifiable task specifications and is crucial for safety-critical control. Yet STL planning remains challenging: exact optimization-based methods are often too slow, and learning-based methods struggle to generalize across varying environments. We propose a zero-shot STL planning solver for variable-map environments that generates feasible trajectories without retraining. By integrating a map-conditioned Transformer architecture with a lightweight heuristic, our approach effectively handles complex disjunctive (OR) subformulas. Furthermore, we leverage Transitive Reinforcement Learning (TRL) to ensure consistent temporal grounding and logical coherence across decomposed sub-tasks. Experiments on dynamic semantic maps with diverse obstacle layouts demonstrate consistent gains, highlighting the framework's superior zero-shot generalization to changing environments and broad STL coverage.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper integrates a map-conditioned Transformer, lightweight heuristic for disjunctive STL, and Transitive RL for zero-shot planning in dynamic maps, but the claims rest on unverified robustness of the heuristic.

read the letter

Map-conditioned Transformer with a heuristic for disjunctive branches and Transitive RL is the main new angle here for zero-shot STL planning in changing maps. The paper does a solid job framing the challenge of generalizing logic-constrained planning without retraining and proposes this architecture to address it. The integration allows handling variable semantic maps by conditioning on the map data, using the heuristic to manage OR subformulas, and TRL to enforce temporal consistency. Experiments reportedly show gains on diverse layouts, which is encouraging for practical robotics applications.

Referee Report

2 major / 1 minor

Summary. The manuscript presents a zero-shot STL planning solver for dynamic semantic maps that integrates a map-conditioned Transformer architecture with a lightweight heuristic to manage complex disjunctive (OR) subformulas in STL specifications. It further employs Transitive Reinforcement Learning (TRL) to maintain temporal grounding and logical coherence across decomposed sub-tasks. The approach is claimed to generate feasible trajectories without requiring retraining or map-specific tuning, with experiments on diverse obstacle layouts showing consistent performance gains and superior generalization.

Significance. Should the central claims be substantiated, this framework could offer a practical advancement in verifiable task planning for robotics, particularly in safety-critical applications where environments change dynamically. By addressing the challenges of disjunctive formulas in STL through a hybrid heuristic-learning approach, it potentially bridges the gap between slow exact methods and non-generalizing learning methods, enabling broader adoption of STL in real-world variable-map scenarios.

major comments (2)

Experiments section: the reported experiments claim consistent gains across diverse obstacle layouts but provide no ablation studies isolating the lightweight heuristic or the TRL component. This omission makes it impossible to verify whether the zero-shot generalization is due to the proposed disjunctive branch selection or other factors, weakening the support for the headline claims.
Method section on disjunctive branch selection: the lightweight heuristic for decomposing disjunctive subformulas is described at a high level without sufficient detail on its decision criteria or handling of non-local reasoning cases. Given that this is load-bearing for the zero-shot claim in unseen maps, a formal specification or algorithm pseudocode is needed to assess its reliability and reproducibility.

minor comments (1)

Abstract: the claim of 'broad STL coverage' is asserted without quantifying the complexity or types of formulas tested; this should be expanded with specific examples or metrics in the main text for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. We address each of the major comments below and indicate the revisions we will make to strengthen the paper.

read point-by-point responses

Referee: Experiments section: the reported experiments claim consistent gains across diverse obstacle layouts but provide no ablation studies isolating the lightweight heuristic or the TRL component. This omission makes it impossible to verify whether the zero-shot generalization is due to the proposed disjunctive branch selection or other factors, weakening the support for the headline claims.

Authors: We agree that ablation studies are important to isolate the contributions of the lightweight heuristic and the TRL component. In the revised version, we will add comprehensive ablation experiments that remove or modify each component individually and report the impact on zero-shot generalization performance across the diverse obstacle layouts. This will provide clearer evidence supporting the role of the disjunctive branch selection in the observed results. revision: yes
Referee: Method section on disjunctive branch selection: the lightweight heuristic for decomposing disjunctive subformulas is described at a high level without sufficient detail on its decision criteria or handling of non-local reasoning cases. Given that this is load-bearing for the zero-shot claim in unseen maps, a formal specification or algorithm pseudocode is needed to assess its reliability and reproducibility.

Authors: We acknowledge that the current description of the disjunctive branch selection heuristic is at a high level. To improve clarity and reproducibility, we will include a formal specification of the decision criteria in the revised method section, along with pseudocode for the algorithm. This will explicitly detail how non-local reasoning cases are handled, thereby better substantiating the zero-shot generalization claims. revision: yes

Circularity Check

0 steps flagged

No circularity: method components and zero-shot claims are independent of fitted inputs or self-referential definitions.

full rationale

The paper proposes a zero-shot STL planner combining a map-conditioned Transformer, a lightweight heuristic for disjunctive subformulas, and Transitive RL for temporal coherence. No equations, fitted parameters, or predictions are described in the abstract or reader's summary that reduce by construction to the inputs (e.g., no parameter fitted on one data subset then renamed as a prediction on related data). The central claims rest on experimental gains across diverse layouts rather than any self-definitional loop or load-bearing self-citation chain. The derivation chain is therefore self-contained against external benchmarks and does not exhibit any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; all components are described at the level of named techniques.

pith-pipeline@v0.9.0 · 5426 in / 944 out tokens · 25897 ms · 2026-05-09T15:03:36.565199+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

22 extracted references · 5 canonical work pages

[1]

Q-learning for robust satisfaction of signal temporal logic specifications

Derya Aksaray, Austin Jones, Zhaodan Kong, Mac Schwager, and Calin Belta. Q-learning for robust satisfaction of signal temporal logic specifications. In55th IEEE Conference on Decision and Control (CDC), pages 6565–6570, 2016

2016
[2]

Formal methods for control synthesis: An optimization perspective.Annual Review of Control, Robotics, and Autonomous Systems, 2(1):115–140, 2019

Calin Belta and Sadra Sadraddini. Formal methods for control synthesis: An optimization perspective.Annual Review of Control, Robotics, and Autonomous Systems, 2(1):115–140, 2019

2019
[3]

Decision transformer: Reinforcement learning via sequence modeling

Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch. Decision transformer: Reinforcement learning via sequence modeling. Advances in neural information processing systems, 34:15084–15097, 2021

2021
[4]

A smooth robustness measure of signal temporal logic for symbolic control.IEEE Control Systems Letters, 5(1):241–246, 2020

Yann Gilpin, Vince Kurtz, and Hai Lin. A smooth robustness measure of signal temporal logic for symbolic control.IEEE Control Systems Letters, 5(1):241–246, 2020

2020
[5]

Offline reinforcement learning as one big sequence modeling problem.Advances in neural information processing systems, 34:1273–1286, 2021

Michael Janner, Qiyang Li, and Sergey Levine. Offline reinforcement learning as one big sequence modeling problem.Advances in neural information processing systems, 34:1273–1286, 2021

2021
[6]

Model- free reinforcement learning for optimal control of markov decision processes under signal temporal logic specifications

Krishna C Kalagarla, Rahul Jain, and Pierluigi Nuzzo. Model- free reinforcement learning for optimal control of markov decision processes under signal temporal logic specifications. In60th IEEE Conference on Decision and Control (CDC), pages 2252–2257, 2021

2021
[7]

Conservative q-learning for offline reinforcement learning.Advances in neural information processing systems, 33:1179–1191, 2020

Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine. Conservative q-learning for offline reinforcement learning.Advances in neural information processing systems, 33:1179–1191, 2020

2020
[8]

Mixed-integer programming for signal temporal logic with fewer binary variables.IEEE Control Systems Letters, 6:2635–2640, 2022

Vincent Kurtz and Hai Lin. Mixed-integer programming for signal temporal logic with fewer binary variables.IEEE Control Systems Letters, 6:2635–2640, 2022

2022
[9]

Karen Leung, Nikos Ar ´echiga, and Marco Pavone. Backpropagation through signal temporal logic specifications: Infusing logical structure into gradient-based methods.The International Journal of Robotics Research, 42(6):356–370, 2023

2023
[10]

Zero-shot trajectory planning for signal temporal logic tasks.arXiv:2501.13457, 2025

Ruijia Liu, Ancheng Hou, Xiao Yu, and Xiang Yin. Zero-shot trajectory planning for signal temporal logic tasks.arXiv:2501.13457, 2025

work page arXiv 2025
[11]

Tgpo: Temporal grounded policy optimization for signal temporal logic tasks,

Yue Meng, Fei Chen, and Chuchu Fan. TGPO: Temporal grounded policy optimization for signal temporal logic tasks.arXiv:2510.00225, 2025

work page arXiv 2025
[12]

Signal temporal logic neural predictive control.IEEE Robotics and Automation Letters, 8(11):7719–7726, 2023

Yue Meng and Chuchu Fan. Signal temporal logic neural predictive control.IEEE Robotics and Automation Letters, 8(11):7719–7726, 2023

2023
[13]

TeLoGraF: Temporal logic planning via graph-encoded flow matching

Yue Meng and Chuchu Fan. TeLoGraF: Temporal logic planning via graph-encoded flow matching. InProceedings of the 42nd International Conference on Machine Learning, pages 43754–43780, 2025

2025
[14]

Offline goal-conditioned reinforcement learning with quasi- metric representations.arXiv:2509.20478, 2025

Vivek Myers, Bill Chunyuan Zheng, Benjamin Eysenbach, and Sergey Levine. Offline goal-conditioned reinforcement learning with quasi- metric representations.arXiv:2509.20478, 2025

work page arXiv 2025
[15]

Wayformer: Motion forecasting via simple & efficient attention networks.arXiv preprint arXiv:2207.05844, 2022

Nigamaa Nayakanti, Rami Al-Rfou, Aurick Zhou, Kratarth Goel, Khaled S Refaat, and Benjamin Sapp. Wayformer: Motion fore- casting via simple & efficient attention networks.arXiv preprint arXiv:2207.05844, 2022

work page arXiv 2022
[16]

Transitive rl: Value learning via divide and conquer.arXiv:2510.22512, 2025

Seohong Park, Aditya Oberai, Pranav Atreya, and Sergey Levine. Transitive rl: Value learning via divide and conquer.arXiv:2510.22512, 2025

work page arXiv 2025
[17]

Model predictive control from signal temporal logic specifications: A case study

Vasumathi Raman, Mehdi Maasoumy, and Alexandre Donz ´e. Model predictive control from signal temporal logic specifications: A case study. InProceedings of the 4th ACM SIGBED international workshop on design, modeling, and evaluation of cyber-physical systems, pages 52–55, 2014

2014
[18]

Robust temporal logic model predic- tive control

Sadra Sadraddini and Calin Belta. Robust temporal logic model predic- tive control. In53rd Annual Allerton Conference on Communication, Control, and Computing, pages 772–779, 2015

2015
[19]

Attention is all you need.Advances in neural information processing systems, 30, 2017

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need.Advances in neural information processing systems, 30, 2017

2017
[20]

Smart: Scalable multi-agent real-time motion generation via next-token prediction

Wei Wu, Xiaoxin Feng, Ziyan Gao, and Yuheng Kan. Smart: Scalable multi-agent real-time motion generation via next-token prediction. Advances in Neural Information Processing Systems, 37:114048– 114071, 2024

2024
[21]

Formal synthesis of controllers for safety-critical autonomous systems: Developments and challenges.Annual Reviews in Control, 57:100940, 2024

Xiang Yin, Bingzhao Gao, and Xiao Yu. Formal synthesis of controllers for safety-critical autonomous systems: Developments and challenges.Annual Reviews in Control, 57:100940, 2024

2024
[22]

Continuous-time control synthesis under nested signal temporal logic specifications

Pian Yu, Xiao Tan, and Dimos V Dimarogonas. Continuous-time control synthesis under nested signal temporal logic specifications. IEEE Transactions on Robotics, 40:2272–2286, 2024

2024