Robix: A Unified Model for Robot Interaction, Reasoning and Planning

ABSTRACT

We introduce Robix, a unified vision-language model designed to serve as the high-level cognitive layer in a hierarchical robot system, integrating robot reasoning, task planning, and natural language interaction within a single architecture. Robix dynamically generates atomic commands for low-level controllers alongside verbal responses for human interaction, enabling end-to-end execution of complex instructions, long-horizon task planning, and natural human-robot collaboration. The model also introduces novel capabilities such as proactive dialogue, real-time interruption handling, and context-aware commonsense reasoning during task execution. At its core, Robix employs chain-of-thought reasoning and is trained through a three-stage strategy: (1) continued pretraining to enhance embodied reasoning skills like 3D spatial understanding, visual grounding, and task-centric reasoning; (2) supervised finetuning to model human-robot interaction and task planning as a unified reasoning-action sequence; and (3) reinforcement learning to improve reasoning-action consistency and long-horizon task coherence. Extensive experiments show that Robix outperforms both open-source and commercial baselines—including GPT-4o and Gemini 2.5 Pro—in interactive task execution, demonstrating strong generalization across diverse instruction types (e.g., open-ended, multi-stage, constrained, invalid, and interrupted) and various user-involved tasks such as table bussing, grocery shopping, and dietary filtering.

AUTHORS

Huang Fang, Mengxi Zhang, Heng Dong, Wei Li, Zixuan Wang, Qifeng Zhang, Xueyun Tian, Yucheng Hu, Hang Li