Post-training

💻 Post-Training Example

Data Preparation

Post-training requires three preparation steps. For a complete guide on customizing your own dataset, see the Custom Data Guide.

Step	Description	Output
1. Prepare LeRobot Dataset	Convert your demonstration data to LeRobot v3.0 format	LeRobot dataset directory
2. Prepare Robot Config	Define feature mapping (states / actions / images) from raw keys to unified feature space	configs/robot_configs/<data_name>.yaml
3. Compute Norm Statistics	Calculate normalization statistics over your dataset	assets/norm_stats/<name>.json

Note: If you already have data in LeRobot v2.1 format, you can use convert_dataset_v21_to_v30.py to quickly convert it to v3.0.

Below we use RoboTwin 2.0 (5 tasks: "open_microwave", "click_bell", "stack_blocks_three", "place_shoe", "put_object_cabinet") as an example.

• Step 1 — RoboTwin Data: Follow RoboTwin2.0 Preparation to download and convert.
• Step 2 — Robot Config: See configs/robot_configs/robotwin.yaml for the RoboTwin feature mapping.
• Step 3 — Normalization: Pre-computed stats are provided at assets/norm_stats/robotwin_50.json. To recompute for a custom task subset, see the Custom Data Guide.

Training

The following example post-trains LingBot-VLA on five RoboTwin 2.0 tasks (open_microwave, click_bell, stack_blocks_three, place_shoe, and put_object_cabinet):

# without depth
bash train.sh tasks/vla/train_lingbotvla.py ./configs/vla/robotwin_load20000h.yaml \
    --data.train_path /path/to/mixed_robotwin_5tasks \
    --data.data_name robotwin \
    --data.norm_stats_file assets/norm_stats/robotwin_50.json \
    --train.output_dir output/

# with depth
bash train.sh tasks/vla/train_lingbotvla.py ./configs/vla/robotwin_load20000h_depth.yaml \
    --data.train_path /path/to/mixed_robotwin_5tasks \
    --data.data_name robotwin \
    --data.norm_stats_file assets/norm_stats/robotwin_50.json \
    --train.output_dir output/

🤖 Real-Robot Post-Training

Also, we provide recommended training configurations specifically tailored for real-world scenarios: real_load20000h.yaml (w/o depth) and real_load20000h_depth.yaml (w/ depth). For a detailed explanation of all training configuration parameters (batch size, gradient accumulation, training duration, checkpointing, depth injection, etc.), see the Training Configuration Guide.