Robix: A Unified Model for Robot Interaction, Reasoning and Planning
Authors: name: Huang Fang, name: Mengxi Zhang, name: Heng Dong, name: Wei Li, name: Zixuan Wang, name: Qifeng Zhang, name: Xueyun Tian, name: Yucheng Hu, name: Hang Li
Organization: ByteDance Seed

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.