I’ve added teleoperation equipment to my Isaac lab, controlling a Franka robotic arm to perform 10 successful stacking demonstrations, then using automatic labeling. However, when I use this dataset to generate mini-batches of examples, I’m finding that it’s misclassifying failed examples as successful ones. This happens even when all the blocks are scattered across the table. I don’t understand why this happens, and could someone tell me how to troubleshoot the issue? Thanks!
Isaac Sim Version
4.5.0
4.2.0
4.1.0
4.0.0
4.5.0
2023.1.1
2023.1.0-hotfix.1
Other (please specify):
Operating System
Ubuntu 22.04
Ubuntu 20.04
Windows 11
Windows 10
Other (please specify):
Steps to Reproduce
step a: create folder for datasets
mkdir -p datasets
step b: collect data with a selected teleoperation device. Replace <teleop_device> with your preferred input device.
Available options: spacemouse, keyboard, handtracking, handtracking_abs, dualhandtracking_abs
./isaaclab.sh -p scripts/tools/record_demos.py --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --teleop_device <teleop_device> --dataset_file ./datasets/dataset.hdf5 --num_demos 10
step c: replay the collected dataset
./isaaclab.sh -p scripts/tools/replay_demos.py --task Isaac-Stack-Cube-Franka-IK-Rel-v0 --dataset_file ./datasets/dataset.hdf5
step d: Self-annotated datasets
./isaaclab.sh -p scripts/imitation_learning/isaaclab_mimic/annotate_demos.py
–device cuda --task Isaac-Stack-Cube-Franka-IK-Rel-Mimic-v0 --auto
–input_file ./datasets/dataset.hdf5 --output_file ./datasets/annotated_dataset.hdf5
step e: Generate mini-batch dataset
./isaaclab.sh -p scripts/imitation_learning/isaaclab_mimic/generate_dataset.py
–device cuda --num_envs 10 --generation_num_trials 10
–input_file ./datasets/annotated_dataset.hdf5 --output_file ./datasets/generated_dataset_small.hdf5
(Add more steps as needed)