Skip to content

Latest commit

 

History

History
46 lines (43 loc) · 2.81 KB

usage.md

File metadata and controls

46 lines (43 loc) · 2.81 KB

Creating the Data Set

Use the following commands to collect human demonstration data for a Visuomotor Policy. Then, run the following script on the host machine.

python3 scripts/sim_demo.py --task=TASK --robot=ROBOT --path=PATH

The task must be specified as TASK and can be one of the following: lid (Closing the lid), cup (Cup shelving), or ladle (Ladle reorganization). The deployment embodiment can be specified as one of the following: abstract, panda, spot, google, or gr1. You should also specify the path to save data as PATH. Collected data would be saved in PATH/RECORDED_TIME.

To post-process the raw demonstration file, please use the following commands.

python3 scripts/sim_proc.py --path=DATA_DIR --task=TASK --robot=ROBOT --mode=sync
python3 scripts/sim_proc.py --path=DATA_DIR --task=TASK --robot=ROBOT --mode=obs
python3 scripts/sim_proc.py --path=DATA_DIR --task=TASK --robot=ROBOT --mode=delta_act
python3 scripts/sim_proc.py --path=DATA_DIR --task=TASK --robot=ROBOT --mode=history_obs
python3 scripts/bc_create_dataset.py --path=DATA_DIR

The dataset is generated in ./data/datasets/dataset_{TASK}.hdf5. This process also generates multiple post-processed hdf5 files. Please use the command below to remove them.

rm -rf ./DATA_DIR/*/demo.hdf5
rm -rf ./DATA_DIR/*/gray_obs.hdf5
rm -rf ./DATA_DIR/*/delta_rpy_act.hdf5
rm -rf ./DATA_DIR/*/history_obs.hdf5

Dataset files consist of sequences of the following data structure.

hdf5 dataset
├── actions: 7D value
└── observation
    ├── right_gray: 128x128x1 array
    ├── left_gray: 128x128x1 array
    ├── delta_positions: 6D value
    └── delta_eulers: 6D value

Training

For training a Visuomotor Policy, please use the following commands.

python3 scripts/bc_train.py --config=PATH_TO_CONFIG --exp=EXPERIMENT_NAME --device=DEVICE --data_path=PATH_TO_DATASET

The configuration at ./config/sim.json would be used for training as the default unless you specify PATH_TO_CONFIG. Trained files would be saved in ./save/EXPERIMENT_NAME/TRAINING_STARTED_TIME. You need to create or download (link) an hdf5-format dataset file and specify the path to the dataset file as PATH_TO_CONFIG.

Evaluation

For evaluating a Visuomotor Policy, please use the following commands.

python3 scripts/sim_evaluate.py --task=TASK --robot=ROBOT --seed=SEED --ckpt_path=PATH_TO_CHECKPOINT

Here, you must specify the path to the pre-trained checkpoint as PATH_TO_CHECKPOINT. The task must be specified as TASK and can be one of the following: lid (Closing the lid), cup (Cup shelving), or ladle (Ladle reorganization). The deployment embodiment, ROBOT, can be specified as one of the following: abstract, panda, spot, google, or gr1.