Initial release

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LICENSE

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+MIT License
+
+Copyright (c) 2026 The RoboTok Authors
+
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+SOFTWARE.
+
+Note: this license covers only the configuration file in this repository
+(models/best_abs_retrieval_model.yaml) and the accompanying source release.
+The trained checkpoints (models/*.pt) and the evaluation keypoints
+(eval_data/torso_relative_clip_keypoints.pt) are derivative works of
+Action100M and are governed by the FAIR Noncommercial Research License v1
+(see LICENSE-Action100M).

LICENSE-Action100M

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README.md

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+---
+license: other
+license_name: fair-noncommercial-research-license-v1
+license_link: https://huggingface.co/RoboTokAnonymous/data_and_models/blob/main/LICENSE-Action100M
+library_name: pytorch
+tags:
+  - robotics
+  - video-retrieval
+  - hand-pose
+  - trajectory
+  - dynamic-time-warping
+  - computer-vision
+  - action100m
+---
+
+# RoboTok — A Scalable Data Engine for Internet Demonstration Video Retrieval and Dexterous Manipulation Learning
+
+🌐 **Project website:** [robotokanonymous.github.io](https://robotokanonymous.github.io/)
+
+![RoboTok hand-motion embedding space](hero.jpg)
+
+Released checkpoints and evaluation keypoints for **RoboTok**, a model that
+retrieves web video clips by 3D hand-motion similarity. Similarity is defined by
+DTW over torso-relative 3D hand keypoints; the encoder is trained to reproduce
+that DTW ranking in a fast embedding space.
+
+Training and evaluation code is in the accompanying source release.
+
+## Files
+
+| File | Size | Description |
+| --- | --- | --- |
+| `models/best_abs_retrieval_model.pt` | 3.8 MB | Retrieval encoder. Cross-attention head over `[T, 126]` hand-trajectory features (21 joints x 3 coords x 2 hands, `T_max = 42`): 1 learned query token, 256-d input projection, 1 cross-attention layer (4 heads, sinusoidal PE), 2-layer MLP to a 256-d embedding. DTW design `abs_21j_coords`. |
+| `models/best_abs_retrieval_model.yaml` | 2 KB | Minimal config to reload the encoder for inference. |
+| `models/body_pose_est.pt` | 9.9 MB | Vector-neuron torso/body-frame estimator: 4-layer rotation-equivariant transformer mapping two-hand trajectories to a torso frame, with separate rotation and translation heads. |
+| `eval_data/torso_relative_clip_keypoints.pt` | 6.5 GB | Torso-relative 3D hand keypoints per clip. Each entry has `video_number`, `node_number`, `node_uid`, `keypoints_per_frame` (`kpts_2d`, `kpts_3d`), and `infilled` / `depth_grounded` flags. |
+
+## Loading
+
+```python
+import torch
+
+ckpt = torch.load("models/best_abs_retrieval_model.pt", map_location="cpu", weights_only=True)
+ckpt["head_state_dict"]   # encoder weights
+ckpt["config"]            # full training configuration
+
+vn = torch.load("models/body_pose_est.pt", map_location="cpu", weights_only=True)
+vn["model"]               # torso estimator weights
+```
+
+## Citation
+
+```bibtex
+@article{anonymous2026robotok,
+  title     = {RoboTok: A Scalable Data Engine for Internet
+               Demonstration Video Retrieval and Dexterous Manipulation
+               Learning},
+  author    = {Anonymous},
+  note      = {Under review},
+  year      = {2026}
+}
+```
+
+## License
+
+**FAIR Noncommercial Research License v1** (see
+[LICENSE-Action100M](LICENSE-Action100M)). Noncommercial research only.
+
+The released checkpoints (`models/*.pt`) and evaluation keypoints
+(`eval_data/torso_relative_clip_keypoints.pt`) are derivative works of
+Action100M (Meta FAIR) clips and are governed by that license. It covers
+trained model weights as "Research Materials", and restricts both those
+materials and any outputs or results obtained from them to noncommercial
+research use. If you publish results obtained using these materials, the
+license requires you to acknowledge that use.
+
+MIT ([LICENSE](LICENSE)) covers only `models/best_abs_retrieval_model.yaml`
+and the accompanying source release.
+
+The MANO / SMPL-H body models required by parts of the pipeline are **not**
+included and remain under their own MPI-IS license terms — register at
+https://mano.is.tue.mpg.de to obtain them.

eval_data/torso_relative_clip_keypoints.pt

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models/best_abs_retrieval_model.pt

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models/best_abs_retrieval_model.yaml

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+# Minimal config for computing embeddings with this checkpoint
+# (see retrieval_training/post_training/get_embeddings.py).
+# Only the fields the builder reads for THIS checkpoint (trajectory-mode,
+# cross-attention head) are kept; everything else falls back to Config defaults
+# and does not affect the exported embeddings. Verified against best_model.pt's
+# head_state_dict (query[1,256], input_projection[256,126], 1 cross-attn layer,
+# 2-layer mlp -> retrieval_dim 256).
+
+# --- correctness (must match the checkpoint / GT) ---
+dtw_design: abs_21j_coords          # validated against the GT design
+input_mode: trajectory              # dataclass default is "jepa" -> must set (silent if wrong)
+retrieval_dim: 256                  # model output dim
+dtw_nn_layer: False                 # selects the cross-attention head (not DTW-NN)
+
+# --- cross-attention head architecture (must match the checkpoint) ---
+trajectory_model_dim: 256           # attention_dim in trajectory mode
+num_cross_attn_heads: 4             # no param-shape effect -> silent if wrong
+num_cross_attn_layers: 1
+num_projection_layers: 2
+num_queries: 1
+use_trajectory_positional_encoding: True   # sinusoidal PE, no params -> silent if wrong
+
+# --- data ---
+# Only used to BUILD trajectories_abs_21j_coords.pt if it isn't already present
+# (in trajectory mode no per-clip jepa features are read). If you pass
+# --trajectories <artifact.pt> to the builder, this path is never touched.
+# Relative to the repository root; set it to a valid local training_data, or
+# supply the trajectories artifact directly.
+data_dir: outputs/training_data

models/body_pose_est.pt

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