# STeP: Signal Temporal Logic for Precise Specifications for Action Generation with Vision Language Models

arXiv: https://arxiv.org/abs/2607.18580 · HTML: https://mlsanity.com/paper/2607.18580/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 36 of its 53 references that are in the map.
- First posted on arXiv 20 Jul 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)
- Vision-language-action models / [Reasoning and chain-of-thought in VLA](https://mlsanity.com/vla/reasoning-and-chain-of-thought-in-vla/)

## Builds on (most cited first, 30 of 36)

- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [GR00T N1: An Open Foundation Model for Generalist Humanoid Robots](https://mlsanity.com/paper/2503.14734/) (2025)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [Inner Monologue: Embodied Reasoning through Planning with Language Models](https://mlsanity.com/paper/2207.05608/) (2022)
- [Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success](https://mlsanity.com/paper/2502.19645/) (2025)
- [VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models](https://mlsanity.com/paper/2307.05973/) (2023)
- [ProgPrompt: Generating Situated Robot Task Plans using Large Language Models](https://mlsanity.com/paper/2209.11302/) (2022)
- [SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics](https://mlsanity.com/paper/2506.01844/) (2025)
- [ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation](https://mlsanity.com/paper/2409.01652/) (2024)
- [Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes](https://mlsanity.com/paper/2103.14127/) (2021)
- [LIBERO-PRO: Towards Robust and Fair Evaluation of Vision-Language-Action Models Beyond Memorization](https://mlsanity.com/paper/2510.03827/) (2025)
- [REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction](https://mlsanity.com/paper/2306.15724/) (2023)
- [Reinforcement Learning With Temporal Logic Rewards](https://mlsanity.com/paper/1612.03471/) (2016)
- [AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers](https://mlsanity.com/paper/2306.06531/) (2023)
- [CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation](https://mlsanity.com/paper/2603.22435/) (2026)
- [OpenHelix: A Short Survey, Empirical Analysis, and Open-Source Dual-System VLA Model for Robotic Manipulation](https://mlsanity.com/paper/2505.03912/) (2025)
- [HYDRA: Hybrid Robot Actions for Imitation Learning](https://mlsanity.com/paper/2306.17237/) (2023)
- [Backpropagation through Signal Temporal Logic Specifications: Infusing Logical Structure into Gradient-Based Methods](https://mlsanity.com/paper/2008.00097/) (2020)
- [A Dual Process VLA: Efficient Robotic Manipulation Leveraging VLM](https://mlsanity.com/paper/2410.15549/) (2024)
- [DeepSTL -- From English Requirements to Signal Temporal Logic](https://mlsanity.com/paper/2109.10294/) (2021)
- [Robust Temporal Logic Model Predictive Control](https://mlsanity.com/paper/1511.00347/) (2015)
- [Signal Temporal Logic Neural Predictive Control](https://mlsanity.com/paper/2309.05131/) (2023)
- [Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMs](https://mlsanity.com/paper/2602.21198/) (2026)
- [Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning](https://mlsanity.com/paper/2402.17217/) (2024)
- [Enhancing Transformation from Natural Language to Signal Temporal Logic Using LLMs with Diverse External Knowledge](https://mlsanity.com/paper/2505.20658/) (2025)
- [Guardian: Detecting Robotic Planning and Execution Errors with Vision-Language Models](https://mlsanity.com/paper/2512.01946/) (2025)
- [Long-Horizon Manipulation via Trace-Conditioned VLA Planning](https://mlsanity.com/paper/2604.21924/) (2026)

## Built on by (most cited first, 2 of 2)

- [SUN: Agentic Robot Policy Learning with Persistent Task Programs](https://mlsanity.com/paper/2608.31167/) (2026)
- [Logic-VLA: A Temporal Logic Conditioned Vision-Language-Action Model](https://mlsanity.com/paper/2608.20556/) (2026)

## Abstract (arXiv)

Natural-language robot instructions often specify more than a coarse task goal: they may impose spatial, temporal, and logical requirements that must remain satisfied throughout execution. We present STeP, a specification-based agentic framework that uses Signal Temporal Logic (STL) as an explicit interface between high-level language reasoning and low-level robot execution. Rather than encoding such requirements implicitly in a learned policy, STeP formalizes them as task specifications that can be decomposed across multi-stage manipulation, enforced during execution, monitored online, and used as structured feedback for replanning. We evaluate STeP on standard LIBERO and LIBERO-PRO, and introduce LIBERO-Constrained, a new benchmark for manipulation tasks with spatial, temporal, and logical requirements, together with real-world tabletop experiments. On LIBERO-PRO, STeP retains 54%-89% success across five of six evaluated perturbation settings, substantially outperforming VLA and code-as-policy baselines under distribution shift. On LIBERO-Constrained, STeP achieves 80% safe success across 49 task-constraint instances; on real-world tasks, it improves safe success over a specification-free model-based baseline across all four task categories, with gains of up to 45 percentage points. These results support explicit formal specifications as a practical interface between foundation-model reasoning and reliable robot execution.