--- pretty_name: AdaDexTrack license: other license_name: component-specific-terms license_link: https://huggingface.co/datasets/Janebek/AdaDexTrack/blob/main/RESOURCE_LICENSES.md tags: - robotics - dexterous-manipulation - motion-tracking - 3d language: - en size_categories: - 1K=0.13.4' python scripts/download_resources.py ``` The downloader resolves one Hub commit, verifies the archive and individual-file checksums, and restores the paths above. It preserves identical installed files and fails on conflicting local files. For a reproducible version, pass `--revision `. To download only the meshes, robot models, textures and object latents: ```bash python scripts/download_resources.py --components assets ``` For manual installation, extract both `.tar.gz` archives into the **code repository root** and put both `bc_tracker_unseen_ref.pth` and `bc_tracker_unseen_obj.pth`, plus `modulator_unseen_ref.pth` and `modulator_unseen_obj.pth`, in `checkpoints/`. Archives already include their top-level `assets/` and `data/adadextrack_full/` paths. Isaac Gym Preview 4 must be obtained and installed separately. ## Optional example specialists ```bash python scripts/download_resources.py --components example_specialists python scripts/collect_specialists.py --specialist-root checkpoints/specialists python scripts/train_bc.py ``` The three example specialists use groups 322 (stamp, two references), 369 (toothpaste, four references), and 435 (wineglass, one reference). Each was trained for 2,001 PPO epochs. Their mean-error success rates are all 100%; framewise Completion rates are 100%, 79.67% and 100%, respectively, using seed 42 and full 300-frame evaluation without initial XY/yaw perturbations. Per-reference metrics and training settings accompany each checkpoint. This component is opt-in and does not overwrite locally trained specialists. See the code repository's `docs/specialists.md` for inference commands. ## Optional object autoencoder The default download installs assets, references, both BC trackers and both modulators. AE weights and AE input meshes are opt-in: ```bash python scripts/download_resources.py --components object_ae object_ae_data ``` The original AE best checkpoint contains a 64-dimensional PointNet encoder and 2,048-point decoder, plus Adam/scheduler state. Its recorded budget is 1,000 epochs; it was selected after epoch 925. The 57 input meshes are split into 55 training inputs and two validation inputs: rubberduck and flute. `data/object_ae/manifest.json` records the split and per-file hashes. These inputs retain the source mesh terms described below. The fixed 50 policy latents in `assets/latents` remain unchanged. Newly seeded sampling/encoding does not promise to reproduce their bytes. See the code repository's `docs/object_ae.md` for AE train, encode, decode and representation compatibility. New representations require consistently retrained policies. ## Resource terms There is **no single blanket license over this mixed resource bundle**. The code repository's BSD license does not replace third-party asset terms. See [RESOURCE_LICENSES.md](RESOURCE_LICENSES.md) for source information and restrictions, and `notices/` for the retained available license files. In particular, GRAB's published terms include noncommercial-use and redistribution restrictions. Including files here does not grant additional rights or establish permission from the original rightsholders. Generated references and trained weights are distinct resources; no blanket relicensing of third-party material is asserted. ## Evaluation Run each tracker with its matching test split from the code repository root: ```bash python scripts/infer_bc.py \ --checkpoint checkpoints/bc_tracker_unseen_ref.pth \ --ref-csv data/adadextrack_full/splits/unseen_trajectory_test.csv \ --batch-size 553 --device cuda:0 --seed 42 python scripts/infer_bc.py \ --checkpoint checkpoints/bc_tracker_unseen_obj.pth \ --ref-csv data/adadextrack_full/splits/unseen_object_test.csv \ --batch-size 245 --device cuda:0 --seed 42 ``` Add `--render` for a viewer. `python scripts/download_resources.py` downloads both BC trackers and both modulators by default; use `--components bc_tracker_unseen_ref bc_tracker_unseen_obj` to download only the two weights. Run the pretrained modulator with its matching frozen BC tracker: ```bash python scripts/infer_modulator.py --dataset unseen_ref \ --checkpoint checkpoints/modulator_unseen_ref.pth \ --tracker-checkpoint checkpoints/bc_tracker_unseen_ref.pth --batch-size 553 python scripts/infer_modulator.py --dataset unseen_obj \ --checkpoint checkpoints/modulator_unseen_obj.pth \ --tracker-checkpoint checkpoints/bc_tracker_unseen_obj.pth --batch-size 245 ``` Add `--render` for a viewer or `--split train` for the corresponding training set. The code uses L2 position errors, quaternion angular errors and 16-finger-joint MAE over all 300 frames. Initial object XY/yaw perturbations are disabled. See the code repository for the exact Mean-error and Completion definitions. ```bibtex @InProceedings{Adalibieke_2026_CVPR, author = {Adalibieke, Jianibieke and Han, Qianwei and Liu, Xueyi and Qin, Yuzhe and Yi, Li}, title = {AdaDexTrack: Dynamic Modulation for Adaptive and Generalizable Dexterous Manipulation Tracking}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, year = {2026} } ```