aiqbench analysis
This paper proposes a Teach-Abstract-Apply framework that lets factory workers program collaborative robots in natural language, with LLMs (open-weight Qwen3-Coder and closed GPT5.2-Codex) generating and reusing skills. The reported gains are that skill abstraction raises task success and cuts interaction cost in a single structure-building case study, with only a preliminary human-in-the-loop check. For AI readers, the useful signal is the LLM-to-robot-skill pipeline and its evaluation setup, not a demonstrated industrial deployment.
Teach, Abstract, Apply: A Framework for Dialogue-Driven Task Learning with Collaborative Robots
arXiv:2611.00297v1 Announce Type: new Abstract: Collaborative robots (cobots) are designed to be safer and more interactive than traditional industrial robots, share the same workspace as humans, and assist them in a range of shop-floor tasks such as palletization, joint assembly, and part inspection. Many of these tasks involve repetitive workflows that may evolve with changes in production environments and task specifications. These requirements motivate the need for intuitive interfaces that allow workers to modify workflows on the shop floor and generalise these routines for future use. This work introduces the Teach-Abstract-Apply (TAA) framework, which enables human operators to (a) teach new workflows to cobots in natural language, (b) abstract the taught workflows into skills for later use, and (c) apply these skills in different settings. As a case study, we apply the TAA framework to a collaborative structure-building task and evaluate it with both open-weight (Qwen3-Coder) and closed (GPT5.2-Codex) models in self-play and cross-model configurations. Our results show that skill abstraction improves task success rates and reduces interaction cost. Beyond automated evaluation, a preliminary human-in-the-loop study suggests that the pipeline also works with human instructions.
Abstract:Collaborative robots (cobots) are designed to be safer and more interactive than traditional industrial robots, share the same workspace as humans, and assist them in a range of shop-floor tasks such as palletization, joint assembly, and part inspection. Many of these tasks involve repetitive workflows that may evolve with changes in production environments and task specifications. These requirements motivate the need for intuitive interfaces that allow workers to modify workflows on the shop floor and generalise these routines for future use. This work introduces the Teach-Abstract-Apply (TAA) framework, which enables human operators to (a) teach new workflows to cobots in natural language, (b) abstract the taught workflows into skills for later use, and (c) apply these skills in different settings. As a case study, we apply the TAA framework to a collaborative structure-building task and evaluate it with both open-weight (Qwen3-Coder) and closed (GPT5.2-Codex) models in self-play and cross-model configurations. Our results show that skill abstraction improves task success rates and reduces interaction cost. Beyond automated evaluation, a preliminary human-in-the-loop study suggests that the pipeline also works with human instructions.
Robotics (cs.RO); Computation and Language (cs.CL)
arXiv:2611.00297 [cs.RO]
(or arXiv:2611.00297v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2611.00297 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 277 words