Actuator Degeneration Adaptation Transformer. In submission progress of DAI 2023.
Total.mp4
- Nvidia IsaacGym
- PyTorch
Details could be viewd in requirements.txt.
-
To train a teacher policy, please follow the example command:
-
python legged_gym/scripts/train.py --task a1_amp --sim_device $DEVICE --rl_device $DEVICE \ --experiment_name $EXP --rum_name $RUN --max_iteration $ITER --joint $JOINT --seed $SEED
-
$JOINTmeans the id of joint whose actuator is suffering the degradation. -
To evaluate, please follow the example command:
-
python legged_gym/scripts/evaluate.py --task a1_amp --sim_device $DEVICE --rl_device $DEVICE \ --experiment_name $EXP --load_run $RUN --checkpoint $CHECKPOINT --file_name $FILE --joint $JOINT
-
If you want to test the performance over all 12 situations, please set
$JOINT=-1. -
To collect dataset, please follow the example command:
-
python legged_gym/scripts/collect.py --task a1_amp --sim_device $DEVICE --rl_device $DEVICE \ --experiment_name $EXP --load_run $RUN --checkpoint $CHECKPOINT --file_name $FILE --joint $JOINT
-
To visualize the performance, please follow the example command:
-
python legged_gym/scripts/play.py --task a1_amp --sim_device $DEVICE --rl_device $DEVICE \ --experiment_name $EXP --load_run $RUN --checkpoint $CHECKPOINT --joint $JOINT --rate $RATE
-
$RATEmeans the degradation rate,-1for randomization.
-
To train a student policy, please follow the example command:
python scripts/train_Adapt.py
The detail args could be viewed in
args.yaml. -
To evaluate, please follow the example command:
python scripts/evaluate_Adapt.py
The detail args could be viewed in
test_args.yaml. -
To visualize the performance, please follow the example command:
python scripts/play_Adapt.py
The detail args could be viewed in
test_args.yaml.