TIC-VLA

TIC-VLA: A Think-in-Control Vision-Language-Action Model for Robot Navigation in Dynamic Environments

Zhiyu Huang^†, Yun Zhang^†, Johnson Liu, Rui Song, Chen Tang, Jiaqi Ma

University of California, Los Angeles (UCLA)
^† Equal contribution

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Overview

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TIC-VLA introduces a latency-aware Think-in-Control (TIC) architecture for vision-language-action (VLA) model for robot navigation in dynamic, human-centric environments.

• 🧠 Think-in-Control Architecture
  Decouples slow vision-language reasoning from fast reactive control through an explicit delayed semantic–control interface.

• ⏱️ Latency-Aware Action Generation
  Conditions control on current observations, cached VLM hidden states, and explicit delay metadata to mitigate stale semantics.

• 🧪 Latency-Consistent Training Pipeline
  Combines vision-language reasoning distillation, latency-induced imitation learning, and online reinforcement learning.

• 🚶 Dynamic, Human-Centric Navigation
  Evaluated in physics-accurate, photo-realistic environments with human-robot interactions and long-horizon instructions.

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Benchmark: DynaNav

We introduce DynaNav, a language-conditioned navigation benchmark designed to test VLA systems under realistic scenarios.

• 85 task configurations across Hospital, Office, Warehouse, and Outdoor scenes
• Varying crowd density, navigation distance, and scene layout

Citation

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@article{huang2026ticvla,
 title={TIC-VLA: A Think-in-Control Vision-Language-Action Model for Robot Navigation in Dynamic Environments},
 author={Zhiyu Huang and Yun Zhang and Johnson Liu and Rui Song and Chen Tang and Jiaqi Ma},
 year={2026}, 
 eprint={2602.02459},
}