Instructions to use MikeWu597/URIS_TurboVLA_PiPER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use MikeWu597/URIS_TurboVLA_PiPER with LeRobot:
- Notebooks
- Google Colab
- Kaggle
URIS_TurboVLA_PiPER
TurboVLA在PiPER真机遥操数据上微调 10000 步的权重。 基座为 MikeWu597/URIS_TurboVLA_Pretrained, 代码采用 H-EmbodVis/TurboVLA。
文件
| 文件 | 说明 |
|---|---|
model.safetensors |
FP32主权重,106.6M 参数(DINOv3 ViT-B 85.7M + 策略头 20.9M) |
ema_model.safetensors |
EMA版本的主权重(decay=0.999) |
config.json |
模型结构超参 + 微调配置 + 7 条任务指令 |
piper_stats.json |
动作/状态归一化统计 |
piper_bert_text_cache.pt |
任务指令缓存 |
loss_curve.png |
微调 loss 曲线(10k 步) |
微调配置
数据采用piper_orbbec_lerobot_joint(LeRobot v2.0)350 episodes,冻结 DINOv3,batch 32 * accum 2(effective 64),lr 2e-5(warmup 500),10k 步
加载方式
import torch
from safetensors.torch import load_file
from turbovla.models.turbovla import build_turbovla
class A: # 与 config.json 一致
LOCAL_DINOV3_PATH = "/path/to/dinov3-vitb16-hf"
text_hidden_dim = 768; hidden_dim = 256; nheads = 8
dim_feedforward = 2048; enhancer_inner_dim = 1024; max_text_len = 256
vla_feature_enhancer_layers = 6; action_dim = 7; chunk_size = 12
state_dim = 8; num_state_tokens = 2
text_dropout = 0.0; fusion_dropout = 0.0; fusion_droptath = 0.1
local_files_only = True; freeze_vision_encoder = True
model = build_turbovla(A())
model.load_state_dict(load_file("model.safetensors"))
model.eval()
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Model tree for MikeWu597/URIS_TurboVLA_PiPER
Base model
MikeWu597/URIS_TurboVLA_Pretrained