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

• URL: http://arxiv.org/abs/2509.01106v2
• Date: Thu, 11 Sep 2025 12:40:54 GMT
• Title: Robix: A Unified Model for Robot Interaction, Reasoning and Planning
• Authors: Huang Fang, Mengxi Zhang, Heng Dong, Wei Li, Zixuan Wang, Qifeng Zhang, Xueyun Tian, Yucheng Hu, Hang Li,
• Abstract summary: Robix is a unified model that integrates robot reasoning, task planning, and natural language interaction within a single vision-language architecture.<n>Robix dynamically generates atomic commands for the low-level controller and verbal responses for human interaction.
• Score: 28.191138548365203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Robix, a unified model that integrates robot reasoning, task planning, and natural language interaction within a single vision-language architecture. Acting as the high-level cognitive layer in a hierarchical robot system, Robix dynamically generates atomic commands for the low-level controller and verbal responses for human interaction, enabling robots to follow complex instructions, plan long-horizon tasks, and interact naturally with human within an end-to-end framework. Robix further introduces novel capabilities such as proactive dialogue, real-time interruption handling, and context-aware commonsense reasoning during task execution. At its core, Robix leverages chain-of-thought reasoning and adopts a three-stage training strategy: (1) continued pretraining to enhance foundational embodied reasoning abilities including 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 demonstrate that Robix outperforms both open-source and commercial baselines (e.g., 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.

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