This repository contains the Hugging Face checkpoint used by
Xiaomi-Robotics-1 for RoboCasa365 evaluation. It includes the model weights,
custom Transformers model and processor code, tokenizer files, and
RoboCasa365 action normalization statistics.
Requirements
The reference environment uses:
Python 3.11
PyTorch 2.8.0
Transformers 4.57.1
FlashAttention 2
The custom model and processor must be loaded with trust_remote_code=True.
Loading
import torch
from transformers import AutoModel, AutoProcessor
model_id = "XiaomiRobotics/Xiaomi-Robotics-1-RoboCasa365"
processor = AutoProcessor.from_pretrained(
model_id,
trust_remote_code=True,
use_fast=False,
)
model = AutoModel.from_pretrained(
model_id,
trust_remote_code=True,
attn_implementation="flash_attention_2",
dtype=torch.bfloat16,
).cuda()
For reproducible multi-GPU evaluation, use the standard eight-server launcher in
the companion source repository.
RoboCasa365 Evaluation
Use the companion Xiaomi-Robotics-1 source repository and follow
eval_robocasa365/README.md. The server launcher accepts either this Hugging
Face repository ID after downloading it locally or a local snapshot path.
Reference configuration:
split: pretrain
task set: target50
tasks: 50
episodes per task: 50
observation history: 4
observation interval: 2
actions per query: 16
environment base seed: 7
crop ratio: 0.95
Reference result:
| Metric |
Value |
| Episodes |
2500 |
| Successes |
1432 |
| Episode success rate |
57.28% |
License
Apache License 2.0. See LICENSE.
