Spatial Logic Enables Precise Robotic Manipulation by Converting Natural Language into Geometric Constraints
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Robotic manipulation often requires precise understanding of spatial relationships and temporal sequences, yet current methods struggle to integrate these elements effectively. Licheng Luo from University of California, Riverside, Yu Xia, and Kaier Liang from Lehigh University, along with Mingyu Cai from University of California, Riverside, address this challenge by introducing a new approach that translates natural language instructions into formal specifications of space and time. Their work centres on Spatio-Temporal Logic, a powerful system for defining geometric requirements, and generates a novel dataset, NL2SpaTiaL, which aligns natural language with complex spatial and temporal objectives. This achievement enables robots to interpret instructions more accurately, verify task completion, and ultimately perform manipulation tasks with greater reliability and compositional understanding, representing a significant step towards more intuitive and robust robotic systems. Natural Language to Formal Specification and Back This research introduces SpaTiaL, a formal language for representing the meaning of natural language instructions, and a system for converting between natural language and SpaTiaL. The system generates logical specifications from everyday language and then renders those specifications back into human-readable text, focusing on a predictable and unambiguous translation process essential for training robotic control systems. This is achieved through a hierarchical generation process, building both the logical form and the natural language in layers. The core innovation lies in the ability to automatically generate paired data of natural language instructions and their corresponding logical forms. The system constructs a tree-like structure of logical operators, defining the overall structure of the logical form, and then fills in the leaves with basic relationships between objects. Simultaneously, it generates natural language phrases corresponding to each node, composing them layer by layer to create a complete sentence, ensuring alignment between the logical form and the natural language. The system’s deterministic rendering, where a single logical form consistently produces the same sentence, is a key advancement, contrasting with many natural language generation systems that produce varied outputs. By controlling the complexity of the generated logical forms and sentences, and using fixed templates for rendering, the system ensures a predictable and consistent translation process. Ultimately, this system provides a sophisticated method for creating high-quality datasets for training models that translate natural language into formal specifications, enabling more accurate and reliable robotic control systems. Hierarchical Logic for Robotic Instruction Translation Researchers have developed NL2SpaTiaL, a new framework that translates natural language instructions into Spatio-Temporal Logic (SpaTiaL) specifications. This work addresses limitations in existing methods by capturing hierarchical spatial relationships crucial for robotic manipulation. The system decomposes instructions into reusable subgoals and spatial constraints, mirroring human task formulation, and constructs a Hierarchical Logical Tree (HLT) to represent complex instructions. A logic-based consistency checker meticulously compares fragments of the original language with corresponding subformulas within the HLT, ensuring semantic accuracy. The framework then composes these structured subformulas into a globally consistent SpaTiaL formula, providing a complete and verifiable representation of the task. The researchers created the NL2SpaTiaL dataset, pairing natural language instructions with flat SpaTiaL formulas, HLT decompositions, and precise span-level alignments. This unique dataset enables systematic evaluation of hierarchical generation and consistency, specifically testing a model’s ability to recover multi-level SpaTiaL specifications from realistic instructions.
Natural Language Specifies Robot Task Logic Researchers have created a new framework for translating complex spatial and temporal requirements into a language robots can understand, significantly improving task specification and execution. This work addresses a critical gap in robotic manipulation, where accurately representing object locations, relationships, and interactions is essential for successful task completion.
The team introduced a novel dataset generation framework that synthesizes “Spatio-Temporal Logic” (SpaTiaL) specifications and converts them into natural language descriptions through a deterministic process. This pipeline creates the “NL2SpaTiaL” dataset, aligning natural language with multi-level spatial relations and temporal objectives, reflecting the compositional structure of manipulation tasks. The generated dataset allows for more accurate representation of complex scenarios, moving beyond simple temporal sequences to encompass layered spatial relationships. A key achievement is the development of a translation-verification framework equipped with a semantic checker, ensuring that the generated SpaTiaL formulas faithfully encode the meaning specified in the original natural language description.
The team demonstrated a deterministic rendering policy that maps SpaTiaL formulas into controlled English, prioritizing structural transparency and semantic preservation, explicitly stating tolerances and bounds for contact and distance relations.
Layered Spatial Logic from Natural Language This research presents NL2SpaTiaL, a new framework that translates natural language instructions into formally defined spatial logic specifications. The researchers addressed a gap in robotic language grounding by developing a method that captures multi-level spatial relations and temporal objectives, reflecting the complexity of manipulation tasks. The framework constructs a hierarchical logical tree, validating each node with a semantic consistency checker to ensure alignment between the language input and the resulting spatial logic. Experiments demonstrate that this layered generation process yields more interpretable and semantically faithful spatial logic specifications compared to single-step translation methods, enabling more robust and verifiable robot control, offering improvements in instruction following for manipulation tasks. 👉 More information 🗞 NL2SpaTiaL: Generating Geometric Spatio-Temporal Logic Specifications from Natural Language for Manipulation Tasks 🧠 ArXiv: https://arxiv.org/abs/2512.13670 Tags: