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arXiv · 2608.22035

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

Wooseong Chung·William Cong·Jakub Dworakowski·Ethan Ewer·Tri Wahyu Guntara·Yeonwoo Jeong·Tianchong Jiang·Chaewon Kim·Hyunseo Kim·Jinwoo Kim·Jinyeon Kim·Yea-Seul Kim·Jack Kunde·Kangwook Lee·Sangheon Lee·Robert Nowak·Junha Roh
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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.

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Wooseong Chung, William Cong, Jakub Dworakowski, Ethan Ewer, Tri Wahyu Guntara, Yeonwoo Jeong, Tianchong Jiang, Chaewon Kim, Hyunseo Kim, Jinwoo Kim, Jinyeon Kim, Yea-Seul Kim, Jack Kunde, Kangwook Lee, Sangheon Lee, Robert Nowak, Junha Roh. 2026-08-22. Ludi${}_{\scriptscriptstyle 0.1}$: An Agentic System for Socially Intelligent Robots. https://arxiv.org/abs/2608.22035

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