Robix: A Unified Model for Robot Interaction, Reasoning and Planning
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.
68b8f795d43cadaf7a688b04
Huang Fang
68b8f795d43cadaf7a688b05
Mengxi Zhang
admin_assigned
2025-09-04T20:11:34.556Z
65f2a7267d5cdbce8882548b
/avatars/f0244fa4e71403d05ea8bfd25986a02b.svg
mengxizhang
user
mengxizhang
68b8f795d43cadaf7a688b06
Heng Dong
claimed_verified
2025-09-04T19:36:52.088Z
64315a3ddd466752c73c2073
/avatars/d9dd119354960bfba424809dab911840.svg
Heng Dong
user
drdh
68b8f795d43cadaf7a688b07
Wei Li
68b8f795d43cadaf7a688b08
Zixuan Wang
68b8f795d43cadaf7a688b09
Qifeng Zhang
68b8f795d43cadaf7a688b0a
Xueyun Tian
68b8f795d43cadaf7a688b0b
Yucheng Hu
68b8f795d43cadaf7a688b0c
Hang Li
Robix: A Unified Model for Robot Interaction, Reasoning and Planning
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.
Robix, a unified vision-language model, integrates robot reasoning, task planning, and natural language interaction, demonstrating superior performance in interactive task execution through chain-of-thought reasoning and a three-stage training strategy.
chain-of-thought reasoning three-stage training strategy continued pretraining supervised finetuning reinforcement learning embodied reasoning 3D spatial understanding visual grounding task-centric reasoning human-robot interaction task planning reasoning-action sequence reasoning-action consistency long-horizon task coherence