Auto-HSI: Personalized human control of a robot swarm on demand by using LLMs for online automatic code generation

2026-09-14

AI Summary

本文提出Auto-HSI方法，通过使用大语言模型在线自动生成代码，解决非专业人员利用自然语言和手势控制机器人集群的问题。

Abstract

This paper presents Auto-HSI, a method for generating personalized human-swarm interaction (HSI) interfaces on demand. The objective is to enable untrained operators to use natural language descriptions and gesture demonstrations to explain how they want the robots to collectively behave in response to their gestures. Based on these inputs, the code should automatically be generated for personalized state machines that will control the robots as desired, in response to the desired gesture inputs. In the developed Auto-HSI prototype, the generated code produces a personalized interface for centralized control using one- and two-handed gestures, enabling a user to teleoperate the robots' motion, formation shape, and shape deformation. We test the gesture tracking and code generation components of Auto-HSI against performance benchmarks. We then test the full Auto-HSI prototype in ``live'' operation experiments, in which real human operators centrally control 50 simulated robots in a physics-based simulator, under nominal and noisy conditions. In these experiments, robots are teleoperated to: score a goal, traverse a maze that requires shape deformation, and score two simultaneous goals by splitting into two groups. We also demonstrate a real human operator making live updates to their personalized Auto-HSI interface during operation (in simulation). Finally, we demonstrate live operation of real robots.

Problem

• Human-Swarm Interaction
• Natural Language
• Gesture Recognition
• Automatic Code Generation

Innovation

• LLMs
• personalized HSI
• automatic code generation
• gesture-based control
• robot swarm