Talk With Machines: Enhancing Human-Robot Interaction Through Large/Vision Language Models

被引:1
作者
Abbas, Ammar N. [1 ]
Beleznai, Csaba [2 ]
机构
[1] Technol Univ Dublin, Sch Comp Sci, Dublin, Ireland
[2] AIT Austrian Inst Technol, Assist & Autonomous Syst, Vienna, Austria
来源
2024 EIGHTH IEEE INTERNATIONAL CONFERENCE ON ROBOTIC COMPUTING, IRC 2024 | 2024年
关键词
large/vision language models; autonomous systems; interpretable robotics;
D O I
10.1109/IRC63610.2024.00039
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Talk With Machines aims to enhance human-robot interaction in safety-critical industrial systems by integrating large/vision language models with robot control and perception. This allows robots to understand natural language commands and perceive their environment. Translating robots' internal states into human-readable text allows operators to gain clearer insights for safer operations. The paper outlines four workflows: low-level control, language-based feedback, visual input, and robot structure-informed task planning, which are presented in a set of experiments. The proposed approach outperforms the prior method in grasping (100% success vs. 90%) and obstacle avoidance (50% success vs. 30%). Supplementary materials are available on the project website: https://talk-machines.github.io.
引用
收藏
页码:253 / 258
页数:6
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