Zero-Shot Signal Temporal Logic Planning with Disjunctive Branch Selection in Dynamic Semantic Maps
- URL: http://arxiv.org/abs/2605.01222v1
- Date: Sat, 02 May 2026 03:32:23 GMT
- Title: Zero-Shot Signal Temporal Logic Planning with Disjunctive Branch Selection in Dynamic Semantic Maps
- Abstract summary: Signal Temporal Logic (STL) offers verifiable task specifications and is crucial for safety-critical control.<n>We propose a zero-shot STL planning solver for variable-map environments that generates feasible trajectories without retraining.
- Score: 5.359659831465926
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Generalizable Multi-Agent Planning from Signal Temporal Logic Specifications via Diffusion [3.7331950863394856]
Multi-agent systems must satisfy rich, temporal tasks while avoiding collisions.<n>Current Signal Temporal Logic (STL) planning methods face critical limitations.<n>We introduce a new diffusion method for multi-agent planning with STL specifications.
arXiv Detail & Related papers (2026-08-30T00:38:31Z) - Navigating the Clutter: Waypoint-Based Bi-Level Planning for Multi-Robot Systems [54.887871365121775]
Multi-robot control in cluttered environments is a challenging problem that involves complex physical constraints.<n>We propose a hybrid multi-robot control framework that jointly optimize task and motion planning.<n>Our approach consistently improves task success over motion-agnostic and VLA-based baselines.
arXiv Detail & Related papers (2026-04-22T22:58:47Z) - DAG-STL: A Hierarchical Framework for Zero-Shot Trajectory Planning under Signal Temporal Logic Specifications [7.389002274709231]
We study offline STL planning under unknown dynamics using only task-agnostic trajectory data.<n>We instantiate this idea in DAG-STL, a hierarchical framework that converts long-horizon STL planning into three stages.<n> Experiments in Maze2D, OGBench AntMaze, and the Cube domain show that DAG-STL substantially outperforms direct robustness-guided diffusion.
arXiv Detail & Related papers (2026-04-20T14:41:37Z) - STL-SVPIO: Signal Temporal Logic guided Stein Variational Path Integral Optimization [8.345095354174623]
Signal Temporal Logic (STL) enables formal specification of complex constraints for robotic task planning.<n>We introduce Signal Vari Temporal Integral Optimization (STL-SVPIO), which reframes STL as a globally informative, differentiable reward-shaping mechanism.<n>We demonstrate that STL-SVPIO significantly outperforms existing methods in both robustness and efficiency for traditional STL tasks.
arXiv Detail & Related papers (2026-03-06T00:53:38Z) - TopoCurate:Modeling Interaction Topology for Tool-Use Agent Training [53.93696896939915]
Training tool-use agents typically rely on Supervised Fine-Tuning (SFT) on successful trajectories and Reinforcement Learning (RL) on pass-rate-selected tasks.<n>We propose TopoCurate, an interaction-aware framework that projects multi-trial rollouts from the same task into a unified semantic quotient topology.<n>TopoCurate achieves consistent gains of 4.2% (SFT) and 6.9% (RL) over state-of-the-art baselines.
arXiv Detail & Related papers (2026-03-02T10:38:54Z) - Zero-Shot Instruction Following in RL via Structured LTL Representations [50.41415009303967]
We study instruction following in multi-task reinforcement learning, where an agent must zero-shot execute novel tasks not seen during training.<n>In this setting, linear temporal logic has recently been adopted as a powerful framework for specifying structured, temporally extended tasks.<n>While existing approaches successfully train generalist policies, they often struggle to effectively capture the rich logical and temporal structure inherent in specifications.
arXiv Detail & Related papers (2026-02-15T23:22:50Z) - LLM-Grounded Dynamic Task Planning with Hierarchical Temporal Logic for Human-Aware Multi-Robot Collaboration [17.886091169216538]
Large Language Models (LLM) enable non-experts to specify openworld multi-robot tasks.<n>LLM plans often lack feasibility and are not efficient, especially in long-horizon scenarios.<n>We propose a neuro-symbolic framework that grounds reasoning into hierarchical specifications.
arXiv Detail & Related papers (2026-02-10T07:11:36Z) - Zero-Shot Instruction Following in RL via Structured LTL Representations [54.08661695738909]
Linear temporal logic (LTL) is a compelling framework for specifying complex, structured tasks for reinforcement learning (RL) agents.<n>Recent work has shown that interpreting instructions as finite automata, which can be seen as high-level programs monitoring task progress, enables learning a single generalist policy capable of executing arbitrary instructions at test time.<n>We propose a novel approach to learning a multi-task policy for following arbitrary instructions that addresses this shortcoming.
arXiv Detail & Related papers (2025-12-02T10:44:51Z) - Unlocking Symbol-Level Precoding Efficiency Through Tensor Equivariant Neural Network [84.22115118596741]
We propose an end-to-end deep learning (DL) framework with low inference complexity for symbol-level precoding.<n>We show that the proposed framework captures substantial performance gains of optimal SLP, while achieving an approximately 80-times speedup over conventional methods.
arXiv Detail & Related papers (2025-10-02T15:15:50Z) - Navigating Motion Agents in Dynamic and Cluttered Environments through LLM Reasoning [69.5875073447454]
This paper advances motion agents empowered by large language models (LLMs) toward autonomous navigation in dynamic and cluttered environments.<n>Our training-free framework supports multi-agent coordination, closed-loop replanning, and dynamic obstacle avoidance without retraining or fine-tuning.
arXiv Detail & Related papers (2025-03-10T13:39:09Z) - Zero-Shot Trajectory Planning for Signal Temporal Logic Tasks [7.389002274709231]
Signal Temporal Logic (STL) is a powerful specification language for describing complex temporal behaviors of continuous signals.<n> generating executable STL plans for STL tasks is challenging, as it requires consideration of the coupling between the task specification and the system dynamics.<n>We propose a hierarchical planning framework that enables zero-shot generalization to new STL tasks by leveraging only task-agnostic trajectory data during offline training.
arXiv Detail & Related papers (2025-01-23T08:15:52Z) - SCoTT: Strategic Chain-of-Thought Tasking for Wireless-Aware Robot Navigation in Digital Twins [78.53885607559958]
We propose SCoTT, a wireless-aware path planning framework.<n>We show that SCoTT achieves path gains within 2% of DP-WA* while consistently generating shorter trajectories.<n>We also show the practical viability of our approach by deploying SCoTT as a ROS node within Gazebo simulations.
arXiv Detail & Related papers (2024-11-27T10:45:49Z) - LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planning [91.95362946266577]
Path planning is a fundamental scientific problem in robotics and autonomous navigation.<n>Traditional algorithms like A* and its variants are capable of ensuring path validity but suffer from significant computational and memory inefficiencies as the state space grows.<n>We propose a new LLM based route planning method that synergistically combines the precise pathfinding capabilities of A* with the global reasoning capability of LLMs.<n>This hybrid approach aims to enhance pathfinding efficiency in terms of time and space complexity while maintaining the integrity of path validity, especially in large-scale scenarios.
arXiv Detail & Related papers (2024-06-20T01:24:30Z) - Funnel-based Reward Shaping for Signal Temporal Logic Tasks in
Reinforcement Learning [0.0]
We propose a tractable reinforcement learning algorithm to learn a controller that enforces Signal Temporal Logic (STL) specifications.
We demonstrate the utility of our approach on several STL tasks using different environments.
arXiv Detail & Related papers (2022-11-30T19:38:21Z) - Goal Kernel Planning: Linearly-Solvable Non-Markovian Policies for Logical Tasks with Goal-Conditioned Options [54.40780660868349]
We introduce a compositional framework called Linearly-Solvable Goal Kernel Dynamic Programming (LS-GKDP)<n>LS-GKDP combines the Linearly-Solvable Markov Decision Process (LMDP) formalism with the Options Framework of Reinforcement Learning.<n>We show how an LMDP with a goal kernel enables the efficient optimization of meta-policies in a lower-dimensional subspace defined by the task grounding.
arXiv Detail & Related papers (2020-07-06T05:13:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.