Teach, Abstract, Apply: A Framework for Dialogue-Driven Task Learning with Collaborative Robots
Teach, Abstract, Apply: A Framework for Dialogue-Driven Task Learning with Collaborative Robots
This work presents the Teach-Abstract-Apply (TAA) framework, enabling human operators to teach collaborative robots new workflows via natural language, abstract these into reusable skills, and apply them across different settings. Evaluated on a structure-building task using both open-weight (Qwen3-Coder) and closed (GPT5.2-Codex) models in self-play and cross-model setups, the framework demonstrates that skill abstraction improves task success rates and reduces interaction costs. Preliminary human-in-the-loop testing indicates compatibility with human instructions.
The paper introduces the Teach-Abstract-Apply (TAA) framework for dialogue-driven task learning with collaborative robots, evaluated via self-play and cross-model configurations using Qwen3-Coder and GPT5.2-Codex, showing improved task success rates and reduced interaction costs in a structure-building case study.
Source: arXiv Robotics — research abstracts · Read original article ↗
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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 spe
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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
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Teach, Abstract, Apply: A Framework for Dialogue-Driven Task Learning with Collaborative Robots
Open source S1
cifications. 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
Open source S3
Source：arXiv Robotics — research abstracts · arxiv.org