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arxiv: 2603.03897 · v3 · submitted 2026-03-04 · 💻 cs.RO · cs.AI· cs.CL· cs.HC· cs.LG

IROSA: Interactive Robot Skill Adaptation using Natural Language

Markus Knauer , Samuel Bustamante , Thomas Eiband , Alin Albu-Sch\"affer , Freek Stulp , Jo\~ao Silv\'erio This is my paper

Pith reviewed 2026-05-15 17:14 UTC · model grok-4.3

classification 💻 cs.RO cs.AIcs.CLcs.HCcs.LG
keywords robot skill adaptationnatural language interfaceslarge language modelstool-based architectureindustrial roboticsimitation learningopen-vocabulary adaptationsafety abstraction
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The pith

A tool-based architecture lets pre-trained language models adapt industrial robot skills through natural language while keeping a safety barrier between the model and hardware.

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

The paper presents a framework that combines pre-trained large language models with imitation learning to adapt robot skills using everyday language commands. It introduces a tool-based system in which the language model selects and parameterizes predefined tools to modify behaviors such as speed, trajectories, or obstacle handling. A protective abstraction layer prevents the model from directly controlling robot hardware, preserving safety and allowing the adaptations to remain transparent and interpretable. The method is shown on a 7-DoF torque-controlled robot executing an industrial bearing ring insertion task, achieving the changes without any fine-tuning of the language model or direct model-to-robot links. A sympathetic reader would care because the approach offers a practical route to flexible, language-driven robot reprogramming in real factory settings.

Core claim

We present a novel framework that enables open-vocabulary skill adaptation through a tool-based architecture, maintaining a protective abstraction layer between the language model and robot hardware. Our approach leverages pre-trained LLMs to select and parameterize specific tools for adapting robot skills without requiring fine-tuning or direct model-to-robot interaction. We demonstrate the framework on a 7-DoF torque-controlled robot performing an industrial bearing ring insertion task, showing successful skill adaptation through natural language commands for speed adjustment, trajectory correction, and obstacle avoidance while maintaining safety, transparency, and interpretability.

What carries the argument

The tool-based architecture that supplies the language model with a curated set of adaptation tools and enforces an abstraction layer so the model never issues direct commands to the robot.

If this is right

  • Robot skills can be modified in real time using natural language without retraining the underlying language model.
  • Safety is preserved because the language model never issues low-level commands directly to the robot hardware.
  • Industrial tasks such as bearing insertion can incorporate on-the-fly changes for speed, path correction, and obstacle avoidance.
  • Adaptations remain transparent because each change traces back to an explicit tool selection and parameterization step.
  • No fine-tuning or additional data collection is required to enable new natural-language-driven modifications.

Where Pith is reading between the lines

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

  • The same tool-selection pattern could support adaptation in other manipulation tasks such as assembly or pick-and-place without redesigning the core interface.
  • Factories could reduce reliance on specialized programmers by letting operators describe desired changes in plain language.
  • Extending the set of available tools might allow the framework to handle more complex constraints like force limits or multi-robot coordination.
  • Repeated real-world deployment would quickly expose whether the language model’s tool choices remain reliable under noisy or ambiguous instructions.

Load-bearing premise

Pre-trained language models will reliably select the correct tools and parameters from natural language inputs without producing errors or unsafe suggestions.

What would settle it

A controlled trial in which the language model receives a command that should trigger an unsafe robot action and it still selects and applies a tool that executes the action on the physical hardware.

Figures

Figures reproduced from arXiv: 2603.03897 by Alin Albu-Sch\"affer, Freek Stulp, Jo\~ao Silv\'erio, Markus Knauer, Samuel Bustamante, Thomas Eiband.

Figure 1
Figure 1. Figure 1: Overview of our approach Interactive RObot Skill Adaptation using natural language. Showing the interactive selection and parameterization of a tool by a LLM based on a user query leading to a skill adaptation via the used execution model. Some of the tools we are providing are shown. ”Respond to User” is a general tool, whereas ”Repulsion Point”, ”Via-Point Insertion” and ”Speed Modulation” are specific t… view at source ↗
Figure 2
Figure 2. Figure 2: Demonstration and prediction analysis for the pick-and-insert task. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Speed adaptation results showing temporal trajectory modification [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Trajectory adaptation through natural language command showing (top) [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Obstacle avoidance through natural language command showing [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
read the original abstract

Foundation models have demonstrated impressive capabilities across diverse domains, while imitation learning provides principled methods for robot skill adaptation from limited data. Combining these approaches holds significant promise for direct application to robotics, yet this combination has received limited attention, particularly for industrial deployment. We present a novel framework that enables open-vocabulary skill adaptation through a tool-based architecture, maintaining a protective abstraction layer between the language model and robot hardware. Our approach leverages pre-trained LLMs to select and parameterize specific tools for adapting robot skills without requiring fine-tuning or direct model-to-robot interaction. We demonstrate the framework on a 7-DoF torque-controlled robot performing an industrial bearing ring insertion task, showing successful skill adaptation through natural language commands for speed adjustment, trajectory correction, and obstacle avoidance while maintaining safety, transparency, and interpretability.

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.

Referee Report

2 major / 1 minor

Summary. The manuscript presents IROSA, a framework for open-vocabulary robot skill adaptation via natural language commands. It uses a tool-based architecture that maintains a protective abstraction layer between pre-trained LLMs and robot hardware, allowing LLMs to select and parameterize adaptation tools without fine-tuning or direct hardware access. The approach is demonstrated on a 7-DoF torque-controlled robot performing an industrial bearing ring insertion task, with qualitative examples of speed adjustment, trajectory correction, and obstacle avoidance while preserving safety and interpretability.

Significance. If supported by quantitative validation, the work could advance safe integration of foundation models in industrial robotics by enabling flexible language-driven skill adaptation without compromising hardware safety. The emphasis on a protective abstraction layer directly addresses reliability and transparency concerns in LLM-robot systems, offering a practical alternative to fine-tuning approaches if the tool-selection mechanism proves robust.

major comments (2)
  1. [Evaluation] Evaluation section: The results consist solely of qualitative successful demonstrations on a single 7-DoF bearing insertion task for speed, trajectory, and avoidance commands. No success rates, failure-mode analysis, baseline comparisons, or statistical measures are reported, leaving the central claim of reliable open-vocabulary adaptation without quantitative support.
  2. [Method] Method and abstract: The protective abstraction layer is presented as ensuring safety by preventing direct LLM-to-hardware interaction, yet no tests under ambiguous, noisy, or adversarial language inputs are described. This assumption is load-bearing for the reliability claim in industrial settings.
minor comments (1)
  1. [Abstract] Abstract: Consider adding a brief statement on the scope of the demonstration (e.g., number of trials or observed edge cases) to better contextualize the qualitative results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. We address the major comments below and will incorporate revisions to strengthen the evaluation and robustness aspects of the work.

read point-by-point responses

  1. Referee: [Evaluation] Evaluation section: The results consist solely of qualitative successful demonstrations on a single 7-DoF bearing insertion task for speed, trajectory, and avoidance commands. No success rates, failure-mode analysis, baseline comparisons, or statistical measures are reported, leaving the central claim of reliable open-vocabulary adaptation without quantitative support.

    Authors: We agree that the current results are qualitative and do not provide quantitative support for the reliability claims. As this is a proof-of-concept demonstration, we will revise the evaluation section to include quantitative metrics, such as success rates across multiple trials for each command type, failure mode analysis, and statistical measures. We will also explore adding a simple baseline comparison if appropriate. revision: yes

  2. Referee: [Method] Method and abstract: The protective abstraction layer is presented as ensuring safety by preventing direct LLM-to-hardware interaction, yet no tests under ambiguous, noisy, or adversarial language inputs are described. This assumption is load-bearing for the reliability claim in industrial settings.

    Authors: We recognize that testing under ambiguous or noisy inputs is crucial for validating the safety of the abstraction layer. In the revised manuscript, we will include additional experiments or simulations demonstrating the system's response to such inputs, including any error handling or fallback strategies. This will better support the reliability claims. revision: yes

Circularity Check

0 steps flagged

No circularity; descriptive framework with qualitative demo only

full rationale

The paper describes a tool-based architecture for open-vocabulary robot skill adaptation via pre-trained LLMs and presents qualitative demonstrations on one industrial task. No equations, derivations, fitted parameters, or predictions appear in the provided text. The central claim rests on system design choices and observed behavior rather than any self-referential reduction, self-citation chain, or ansatz smuggled through prior work. Self-citations, if present, are not load-bearing for the architecture itself. This is a standard non-circular presentation of an engineering framework.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The framework depends on the domain assumption that LLMs can map language to tool selection and parameterization reliably enough for industrial use; the tool architecture itself is an invented protective layer with no independent evidence beyond the single demonstration.

axioms (1)
  • domain assumption Pre-trained LLMs possess sufficient understanding of robot task contexts to select and parameterize adaptation tools from natural language without fine-tuning.
    Invoked in the description of how the LLM chooses tools for speed adjustment, trajectory correction, and obstacle avoidance.
invented entities (1)
  • Tool-based architecture with protective abstraction layer no independent evidence
    purpose: To isolate the language model from direct robot hardware control while enabling skill adaptation.
    New component introduced to maintain safety, transparency, and interpretability; no external falsifiable evidence provided beyond the described demonstration.

pith-pipeline@v0.9.0 · 5463 in / 1273 out tokens · 39960 ms · 2026-05-15T17:14:25.801388+00:00 · methodology

discussion (0)

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