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ARXIV:2604.04233 · ROBOTICS COMMAND UNDERSTANDING · SUBMITTED 07 APR · 20:12 UTC · FRESHNESS UNKNOWN
ARXIV:2604.04233ROBOTICS COMMAND UNDERSTANDINGSUBMITTED 07 APR · 20:12 UTCFRESHNESS UNKNOWNXinyun Huo · Raghav Gnanasambandam · Xinyao Zhang · arXiv
A grammar-constrained LLM for precise, robot-readable industrial commands, improving safety and efficiency in human-robot collaboration.
Opportunity summary
Pain A grammar-constrained LLM for precise, robot-readable industrial commands, improving safety and efficiency in human-robot collaboration.
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A grammar-constrained LLM for precise, robot-readable industrial commands, improving safety and efficiency in human-robot collaboration. While Large Language Models (LLMs) understand general language, they often lack the domain-specific rigidity needed for safe and executable…
Human-robot collaboration in industrial settings requires precise and reliable communication to enhance operational efficiency. While Large Language Models (LLMs) understand general language, they often lack the domain-specific rigidity needed for safe and executable industrial…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To address this gap, this paper introduces a novel grammar-constrained LLM that integrates a grammar-driven Natural Language Understanding (NLU) system with a fine-tuned LLM,…
Robotics Command Understanding moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A grammar-constrained LLM for precise, robot-readable industrial commands, improving safety and efficiency in human-robot collaboration.
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10.48550/arXiv.2604.04233A grammar-constrained LLM for precise, robot-readable industrial commands, improving safety and efficiency in human-robot collaboration.
Abstract
Human-robot collaboration in industrial settings requires precise and reliable communication to enhance operational efficiency. While Large Language Models (LLMs) understand general language, they often lack the domain-specific rigidity needed for safe and executable industrial commands. To address this gap, this paper introduces a novel grammar-constrained LLM that integrates a grammar-driven Natural Language Understanding (NLU) system with a fine-tuned LLM, which enables both conversational flexibility and the deterministic precision required in robotics. Our method employs a two-stage process. First, a fine-tuned LLM performs high-level contextual reasoning and parameter inference on natural language inputs. Second, a Structured Language Model (SLM) and a grammar-based canonicalizer constrain the LLM's output, forcing it into a standardized symbolic format composed of valid action frames and command elements. This process guarantees that generated commands are valid and structured in a robot-readable JSON format. A key feature of the proposed model is a validation and feedback loop. A grammar parser validates the output against a predefined list of executable robotic actions. If a command is invalid, the system automatically generates corrective prompts and re-engages the LLM. This iterative self-correction mechanism allows the model to recover from initial interpretation errors to improve system robustness. We evaluate our grammar-constrained hybrid model against two baselines: a fine-tuned API-based LLM and a standalone grammar-driven NLU model. Using the Human Robot Interaction Corpus (HuRIC) dataset, we demonstrate that the hybrid approach achieves superior command validity, which promotes safer and more effective industrial human-robot collaboration.
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Dimensions overall score 7.0
PROBLEM
A grammar-constrained LLM for precise, robot-readable industrial commands, improving safety and efficiency in human-robot collaboration. While Large Language Models (LLMs) understand general language, they often lack the domain-specific rigidity needed for safe and executable in...
METHOD
Human-robot collaboration in industrial settings requires precise and reliable communication to enhance operational efficiency. While Large Language Models (LLMs) understand general language, they often lack the domain-specific rigidity needed for safe and executable industrial...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To address this gap, this paper introduces a novel grammar-constrained LLM that integrates a grammar-driven Natural Language Understanding (NLU) system with a fine-tuned LLM, which enables both conversati...
WHY NOW
Robotics Command Understanding moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
A grammar-constrained LLM for precise, robot-readable industrial commands, improving safety and efficiency in human-robot collaboration. While Large Language Models (LLMs) understand general language, they often lack the domain-specific rigidity needed for safe and executable industrial commands.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Human-robot collaboration in industrial settings requires precise and reliable communication to enhance operational efficiency. While Large Language Models (LLMs) understand general language, they often lack the domain-specific rigidity needed for safe and executable industrial commands.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To address this gap, this paper introduces a novel grammar-constrained LLM that integrates a grammar-driven Natural Language Understanding (NLU) system with a fine-tuned LLM, which enables both conversational flexibility and the deterministic precision required in robotics. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Robotics Command Understanding moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A grammar-constrained LLM for precise, robot-readable industrial commands, improving safety and efficiency in human-robot collaboration.
Segment
Robotics Command Understanding
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7.0/10 public viability
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Technical feasibility
partial
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