Ludi${}_{\scriptscriptstyle 0.1}$: An Agentic System for Socially Intelligent Robots

2026-08-22

AI Summary

为实现自然的人机协作，提出Ludi系统，整合了多模态推理、记忆等功能，并通过微调视觉-语言模型来处理复杂交互情况。

Abstract

Robot foundation models have substantially advanced perception and control, but natural human-robot collaboration requires more than executing isolated commands. A robot must recognize ambiguity, maintain context across turns, communicate its intentions, and revise ongoing behavior as the user's intent changes. We present $\scriptstyle\mathsf{Ludi}_{\scriptscriptstyle 0.1}$, an agentic system for socially intelligent robots that integrates interactive speech, multimodal reasoning, memory, navigation, and learned manipulation. Its decision-making core is a fine-tuned vision-language model trained on multi-turn interaction traces spanning ambiguous requests, clarifications, corrections, interruptions, mixed social and task dialogue, and multi-step tasks. A purpose-built harness manages the model-tool interaction loop, while specialized navigation and manipulation policies execute physical skills. Ludi${}_{\scriptscriptstyle 0.1}$ demonstrates a practical path toward fluid human-robot collaboration today while producing the multimodal interaction traces needed to develop a more deeply integrated foundation model for robots and people.

Problem

• socially intelligent robots
• human-robot collaboration
• ambiguous requests
• multimodal interaction
• context maintenance

Innovation

• interactive speech
• multimodal reasoning
• fine-tuned vision-language model
• multi-turn interaction traces
• agentic system