arXiv cs.CL· Chalamalasetti Kranti, Sherzod Hakimov, David Schlangen·· 11 小时前AI 评分29
Teach-Abstract-Apply：面向协作机器人的对话驱动任务学习框架
Teach, Abstract, Apply: A Framework for Dialogue-Driven Task Learning with Collaborative Robots
AI 导读
研究提出 Teach-Abstract-Apply（TAA）框架，让操作者用自然语言向协作机器人教授新工作流，将其抽象为可复用技能并在不同场景中应用。在协作结构搭建任务上，开源权重模型 Qwen3-Coder 与闭源模型 GPT5.2-Codex 的自对弈和跨模型配置评测显示，技能抽象提升了任务成功率并降低交互成本。初步的人类在环研究还表明，该流程同样适用于人类指令。
正文 · 原文
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.
| Comments: | Preprint |
| Subjects: | Robotics (cs.RO); Computation and Language (cs.CL) |
| Cite as: | arXiv:2611.00297 [cs.RO] |
| (or arXiv:2611.00297v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2611.00297 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Kranti Chalamalasetti [view email]
[v1]
Fri, 30 Oct 2026 19:21:30 UTC (958 KB)
来源：arXiv cs.CL · arxiv.org