--- license: cc-by-4.0 task_categories: - robotics tags: - robotics - tactile - manipulation - multimodal - gelsight - realsense - motion-capture - dynamics - world-model - human-collected pretty_name: React (Tactile-Visual Manipulation) size_categories: - 100K **106 min of robot-free human-hand multimodal interaction · 190,231 frames @ 30 Hz across 3 × RGB-D + 2 × GelSight + 3-body OptiTrack** ## What's different about this dataset | | | |---|---| | **Robot-arm-free** | Recorded directly from a human operator holding two GelSight Mini sensors. No robot kinematics, no embodiment bias, no robot occluding the scene. | | **Tactile + RGB-D + mocap, simultaneous** | Most manipulation datasets ship one of these. React ships all three, synchronized to a common 30 Hz clock. | | **Contact-dense** | **64 % of post-trim frames** have confirmed tactile contact on at least one sensor — see [`figures/contact_intensity_full.png`](figures/contact_intensity_full.png). | | **Long, continuous interaction** | Recordings are minutes long, not seconds. Median recording duration is 4 min; longest 19 min. Good for short-window sampling of dynamics, not for action-conditioned policy learning. | ![Comparison with other manipulation datasets](figures/dataset_figures/F7_comparison_table.png) ## At a glance | | | |---|---| | Embodiment | **Human hands (no robot)** — handheld GelSight sensors with motion-capture rigid bodies | | Intended use | Dynamics / world-model learning over short multimodal windows. Sample short trajectories (1 s – 10 s); recording-file boundaries are not action boundaries. | | Total synchronized duration | **105.7 min** at 30 Hz (190,231 multimodal frames, post-trim) | | Bimanual tactile-contact time | **64.3 % of post-trim frames** (3,302 contact events, median 0.73 s; see [`figures/dataset_figures/F2_contact_event_duration_histogram.png`](figures/dataset_figures/F2_contact_event_duration_histogram.png) and [`metadata/episodes.parquet`](metadata/episodes.parquet) for per-file numbers) | | Cameras | 3× Intel RealSense D415 (color + depth), 480×640, 30 FPS | | Tactile | 2× GelSight Mini (left, right), handheld | | Motion capture | OptiTrack VRPN, 3 rigid bodies, ~120 Hz | | Tasks | `motherboard` (more coming) | | License | CC-BY-4.0 | ## Recording sessions | Date | Kind | Active sensors | Notes | |---|---|---|---| | 2026-05-10 | session | left + right | First full bimanual session. | | 2026-05-11 | session | left + right | Largest session. A handful of GelSight LED-flicker frames + one mocap teleport; see [`bad_frames.json`](bad_frames.json). | | 2026-05-19 | session | left + right | New session, multi-cam (`view_left/middle/right`) end-to-end. Curation via reproducible `detect_bad_intervals.py` ruleset (see [`docs/curation_pipeline.md`](docs/curation_pipeline.md)). | See [`tasks.json`](tasks.json) for the machine-readable registry (per-date `active_sensors`, etc.). **OT-uninitialized prefixes trimmed.** Three episodes had OptiTrack offline at the start of recording (1–11 min each); those prefixes have been cut from the published `.pt` files (`_contact_meta.trim_offset` per file). Future recordings use an OT watchdog that refuses to start an episode unless mocap is streaming. Full story: [`docs/caveats.md`](docs/caveats.md). ## Data quality | Mode | Frames | % | Files | Cause | |---|---:|---:|---:|---| | GelSight LED flicker | 56 | 0.029 % | 5 | Single-frame LED dropout, recovers next frame | | OptiTrack pose teleport | 56 | 0.029 % | 3 | Solver flip (translation > 5 m/s or angular > 15 rad/s) | | OptiTrack track loss | 1,680 | 0.883 % | 6 | Marker briefly left mocap-volume / camera FOV mid-episode | | **Total (union)** | **1,768** | **0.929 %** | **11** | | Every flagged interval is in [`bad_frames.json`](bad_frames.json) keyed by `episode/episode_*` with TRIMMED-pt frame indices. A richer per-event view (with cross-modal motion + OT-gap + angular-velocity stats) lives in [`freeze_intervals.json`](freeze_intervals.json). Skip-list usage is shown below and in [`docs/quality.md`](docs/quality.md). Long start-of-episode OT-uninitialized prefixes (the dominant problem in the raw recordings) have already been trimmed from the published `.pt` files — see [`docs/caveats.md`](docs/caveats.md). ## Two layouts: `episodes/` vs `segments/` The same recordings are shipped two ways depending on what your code wants to do: - **`episodes///episode_*.pt`** — one file per recording. Includes bad intervals (LED flicker, pose teleport, OT track loss) inside; downstream code is expected to filter them out using `bad_frames.json`. Each file carries all three RealSense views (`view_left`, `view_middle`, `view_right`) plus both GelSights. - **`segments///episode_*.segment_*.pt`** — same recordings, but **pre-sliced into contiguous clean segments at every bad-frames boundary**. No `bad_frames.json` lookup needed; the data is clean by construction. Index lookup via [`segments.json`](segments.json). Each segment's `_contact_meta.source_h5_frame_range` maps it back to the original recording. The example `ReactSegmentDataset` ([`examples/react_segment_dataset.py`](examples/react_segment_dataset.py)) consumes these. Both layouts have identical content (same source recordings, same frame data); only the file boundaries differ. ## Quick start ```python # Load by task with `datasets` from datasets import load_dataset ds = load_dataset("yxma/React", "motherboard", split="train") ``` Or grab a single recording file directly: ```python import torch from huggingface_hub import hf_hub_download path = hf_hub_download( repo_id="yxma/React", repo_type="dataset", filename="episodes/motherboard/2026-05-11/episode_003.pt", ) ep = torch.load(path, weights_only=False) # ep["view"] (T, 3, 128, 128) uint8 — overhead cam # ep["tactile_left"], ep["tactile_right"] (T, 3, 128, 128) uint8 # ep["sensor_left_pose"], ep["sensor_right_pose"] # (T, 7) float32 — xyz + quaternion # ep["timestamps"] (T,) float64 # Plus per-frame contact metrics: tactile_{side}_{intensity, area, mixed} ``` Sampling short windows for dynamics learning — **drop windows that overlap any flagged interval**: ```python import json with open("bad_frames.json") as f: bad = json.load(f)["episodes"] # frame indices are TRIMMED-pt coordinates def is_clean_window(episode_key, t_start, t_end): """True iff [t_start, t_end] doesn't intersect any flagged span.""" bf = bad[episode_key] intervals = (bf["intensity_spikes"] + bf["pose_teleports_L"] + bf["pose_teleports_R"] + bf["ot_loss_L"] + bf["ot_loss_R"]) return all(not (s <= t_end and e >= t_start) for s, e in intervals) ``` Currently 1,768 / 190,231 frames (0.93 %) are flagged across 11 of 27 files — see [`docs/quality.md`](docs/quality.md) for the per-mode breakdown and more filtering recipes. The example dataloader below does this filtering for you when `skip_bad_frames=True`. ## Example dataloader — short contact-rich windows A reference PyTorch `Dataset` is shipped under [`examples/react_window_dataset.py`](examples/react_window_dataset.py). It scans the processed `.pt` files, applies the contact filter, drops windows that overlap [`bad_frames.json`](bad_frames.json), and respects the per-date `active_sensors` field from [`tasks.json`](tasks.json). ```python from examples.react_window_dataset import ReactWindowDataset from torch.utils.data import DataLoader ds = ReactWindowDataset( data_root="episodes/motherboard", bad_frames_path="bad_frames.json", tasks_json_path="tasks.json", window_length=16, # frames per window stride=1, # within-window stride (1 = consecutive) window_step=16, # step between window starts (overlap control) contact_metric="mixed", # "intensity" | "area" | "mixed" tactile_threshold=0.4, min_contact_fraction=0.6, # ≥ 60 % of window frames must have contact which_sensors="any", # "any" | "both" | "left" | "right" skip_bad_frames=True, respect_active_sensors=True, ) print(len(ds), "windows") loader = DataLoader(ds, batch_size=8, shuffle=True, num_workers=2) ``` With the defaults shown above, the dataset assembles **~9.2 k contact-rich 16-frame windows** across the 27 recordings. Each sample is a dict of `(T, …)` tensors plus metadata (`episode`, `frame_start`, `active_sensors`, …). ### Example output Four random windows, time runs left→right; each cell is `view | tactile_left | tactile_right` with sensor frame axes (X red, Y green, Z blue-ish) projected onto the view: ![dataloader sample grid](figures/dataloader_examples/sample_grid.png) One window played frame-by-frame with the sensor-frame overlay: ![dataloader sample GIF](figures/dataloader_examples/sample_window.gif) Full demo script: [`examples/demo_react_window.py`](examples/demo_react_window.py). ## Recording-file previews Per-episode previews live under [`figures/episode_previews/`](figures/episode_previews/) as inline-renderable MP4s. **Browse all 32 episodes (collapsed by default) on [](figures/episode_previews/index.md) — click any row to preview that episode inline.** Each shows 60 frames evenly sampled across the episode in the recording-viewer layout: 3 RealSense cameras with projected GelSight axes, GelSight raw + diff thumbs, OptiTrack pose text panel. (The on-disk recording unit is called an "episode" purely for file naming — these boundaries don't carry semantic / action meaning for this dataset.) ## Repository layout ``` README.md # this file tasks.json # task / session registry bad_frames.json # data-quality skip-list episodes///episode_*.pt # per-file tensors figures/ # previews + analysis figures docs/ # extended documentation ``` ## More documentation | File | Contents | |---|---| | [`docs/recording.md`](docs/recording.md) | Hardware setup, camera serials, sensor + mocap layout, robot-free collection method | | [`docs/schema.md`](docs/schema.md) | Full `.pt` field reference and contact-metric definitions | | [`docs/quality.md`](docs/quality.md) | Data-quality breakdown (per-mode), `bad_frames.json` schema, dataloader recipe, inspection figures | | [`docs/figures.md`](docs/figures.md) | Dataset statistics + analysis gallery (F1–F8) | | [`docs/caveats.md`](docs/caveats.md) | Known caveats and roadmap | ## License Released under [Creative Commons Attribution 4.0](https://creativecommons.org/licenses/by/4.0/) (CC-BY-4.0). ## Citation If you use this dataset, please cite (TODO: add bibtex).