---
title: Xiaomi-Robotics-0-SimplerEnv-Google-Robot
canonical_url: "https://www.modelscope.ai/models/XiaomiRobotics/Xiaomi-Robotics-0-SimplerEnv-Google-Robot"
md_url: "https://www.modelscope.ai/models/XiaomiRobotics/Xiaomi-Robotics-0-SimplerEnv-Google-Robot.md"
repository: XiaomiRobotics/Xiaomi-Robotics-0-SimplerEnv-Google-Robot
last_updated: 2026-07-29
license: apache-2.0
model_type:
  - mibot
architectures:
  - MiBoTForActionGeneration
parameters: 4.7B
tensor_type:
  - BF16
library_name:
  - pytorch
  - transformer
  - safetensors
frameworks:
  - pytorch
downloads: 214
stars: 0
tags:
  - vla
  - vision-language-action
  - real-time
---

# Xiaomi-Robotics-0-SimplerEnv-Google-Robot

> Xiaomi-Robotics-0-SimplerEnv-Google-Robot - An open-source model by XiaomiRobotics on ModelScope. Xiaomi-Robotics-0 is an advanced Vision-Language-Action (VLA) model with 4.7B parameters, specifically engineered for high-performance robotic reasoning and…

XiaomiRobotics/Xiaomi-Robotics-0-SimplerEnv-Google-Robot is a 4.7B-parameter machine learning model on ModelScope. licensed under apache-2.0.

- **Repository**: XiaomiRobotics/Xiaomi-Robotics-0-SimplerEnv-Google-Robot
- **License**: apache-2.0
- **Parameters**: 4.7B
- **Tags**: vla, vision-language-action, real-time
- **Downloads**: 214
- **Stars**: 0
- **Last updated**: 2026-07-29

Source: https://www.modelscope.ai/models/XiaomiRobotics/Xiaomi-Robotics-0-SimplerEnv-Google-Robot

---

# Xiaomi-Robotics-0

**Xiaomi-Robotics-0** is an advanced Vision-Language-Action (VLA) model with 4.7B parameters, specifically engineered for high-performance robotic reasoning and seamless real-time execution.

- **Paper:** [Xiaomi-Robotics-0: An Open-Sourced Vision-Language-Action Model with Real-Time Execution](https://huggingface.co/papers/2602.12684)
- **Project Page:** [xiaomi-robotics-0.github.io](https://xiaomi-robotics-0.github.io/)
- **Repository:** [GitHub - Xiaomi-Robotics-0](https://github.com/XiaomiRobotics/Xiaomi-Robotics-0)

## Model Description
Xiaomi-Robotics-0 is first pre-trained on large-scale cross-embodiment robot trajectories and vision-language data, providing it with broad and generalizable action-generation capabilities. It utilizes a carefully designed training recipe and deployment strategy to address inference latency, enabling fast and smooth real-time rollouts on consumer-grade GPUs.

## Quick Start: Deployment

The model is compatible with the Hugging Face `transformers` ecosystem. By leveraging Flash Attention 2 and bfloat16 precision, it can be run efficiently for robotic manipulation tasks.

```python
import torch
from transformers import AutoModel, AutoProcessor

# 1. Load model and processor 
model_path = "XiaomiRobotics/Xiaomi-Robotics-0"
model = AutoModel.from_pretrained(
    model_path, 
    trust_remote_code=True, 
    attn_implementation="flash_attention_2", 
    dtype=torch.bfloat16
).cuda().eval()
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True, use_fast=False)


# 2. Construct the prompt with multi-view inputs
language_instruction = "Pick up the red block."
instruction = (
    f"<|im_start|>user
The following observations are captured from multiple views.
"
    f"# Base View
<|vision_start|><|image_pad|><|vision_end|>
"
    f"# Left-Wrist View
<|vision_start|><|image_pad|><|vision_end|>
"
    f"Generate robot actions for the task:
{language_instruction} /no_cot<|im_end|>
"
    f"<|im_start|>assistant
<cot></cot><|im_end|>
"
)

# 3. Prepare inputs
# Assuming `image_base`, `image_wrist`, and `proprio_state` are already loaded
inputs = processor(
    text=[instruction],
    images=[image_base, image_wrist], # [PIL.Image, PIL.Image]
    videos=None,
    padding=True,
    return_tensors="pt",
).to(model.device)

# Add proprioceptive state and action mask
robot_type = "libero"
inputs["state"] = torch.from_numpy(proprio_state).to(model.device, model.dtype).view(1, 1, -1)
inputs["action_mask"] = processor.get_action_mask(robot_type).to(model.device, model.dtype)

# 4. Generate action 
with torch.no_grad():
    outputs = model(**inputs)
    
# Decode raw outputs into actionable control commands
action_chunk = processor.decode_action(outputs.actions, robot_type=robot_type)
print(f"Generated Action Chunk Shape: {action_chunk.shape}")
```

## Benchmark Results

The model has been evaluated extensively on standard simulation benchmarks:

| Benchmark | Description | Performance |
| :--- | :--- | :--- |
| **LIBERO** | Fine-tuned on four LIBERO suites. | **98.7%** (Avg Success) |
| **CALVIN** | Fine-tuned on ABCD→D Split. | **4.80** (Avg Length) |
| **SimplerEnv** | Fine-tuned on Fractal dataset (Google Robot). | **85.5%** (VM) / **74.7%** (VA) |
| **SimplerEnv** | Fine-tuned on Bridge dataset (WidowX). | **79.2%** |

## Citation

```bibtex
@misc{robotics2026xiaomi,
  title        = {Xiaomi-Robotics-0: An Open-Sourced Vision-Language-Action Model with Real-Time Execution},
  author       = {Xiaomi Robotics},
  howpublished={\url{https://xiaomi-robotics-0.github.io}},
  year         = {2026},
  note={Project Website}
}
```