MistyPilot: Enabling Social-Robot Control through Multi-Agent LLM Skill Orchestration

2026-08-16

AI Summary

This study addresses the challenges of task complexity, API orchestration, and cumbersome state management in natural language programming for social robots by proposing a multi-agent LLM framework. The system employs a dual-agent architecture to decouple physical and social interactions, utilizing task routing alongside sensor-skill binding and result reuse mechanisms to enable context-aware skill orchestration and efficient state management. Experimental results demonstrate superior performance in routing accuracy and scalability with low variance. Furthermore, user studies confirm the framework’s high usability and interaction quality, effectively enhancing both the efficiency and stability of robot programming.

Abstract

Programming small social robots from natural-language instructions requires more than invoking isolated APIs. Interactive tasks combine reactive physical behaviors with stateful social behaviors, while existing interfaces often require developers to manually compose APIs into skills, configure their parameters, bind sensor events to skills, and manage task states at runtime. We present MistyPilot, a multi-agent LLM framework that interprets high-level natural-language instructions and orchestrates the corresponding skills on the Misty social robot. A Task Router dispatches each instruction to one of two specialized agents: a Physically Interactive Agent for sensor-triggered robot control and direct skill invocation, and a Social Interaction Agent for dialogue-oriented task-state management and context-dependent multimodal response generation. To improve efficiency, the Social Interaction Agent reuses previously generated results when applicable and invokes full generation otherwise. We evaluate MistyPilot on five component-level suites, with sensor bindings and skill invocations executed on the physical Misty robot, and a preliminary user study with 12 participants. MistyPilot attains high accuracy on routing, sensor-skill binding, task-state parsing, result reuse, and skill extension up to 100 skills, and lower variance than an otherwise identical single-agent baseline, while participants report positive perceptions of usability and interaction quality. The code will be made publicly available via the project page.

Problem

• Social Robot Control
• Natural Language Instructions
• Skill Orchestration
• Interactive Tasks

Innovation

• Multi-Agent LLM Orchestration
• Social Robot Control
• Task Router
• Result Reuse Mechanism
• Natural Language Programming