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Convert between robotics dataset formats with one command. Score demonstration quality with research-backed metrics. Segment episodes into sub-skills with changepoint detection.

*RoboDM requires manual installation from GitHub (see below)

See docs/model_formats.md for which models (Octo, OpenVLA, ACT, Diffusion Policy, etc.) use which format. See docs/format_reference.md for detailed format specifications.

Every robotics lab has their own data format: Open-X uses RLDS, HuggingFace uses LeRobot, Diffusion Policy uses Zarr, robomimic uses HDF5. Want to train Octo on your ALOHA data? Write a converter. Want to use LeRobot on Open-X datasets? Write another.

Forge uses a hub-and-spoke architecture — one intermediate representation, O(n) format support:

Any Reader → Episode/Frame → Any Writer

Add a reader, get all writers for free. Add a writer, get all readers for free. No N×M conversion logic. See docs/architecture.md for details.

git clone https://github.com/arpitg1304/forge.git
cd forge
pip install -e ".[all]"

RoboDM requires manual installation from GitHub (PyPI version has a codec bug):

git clone https://github.com/BerkeleyAutomation/robodm.git
pip install -e robodm

# See what's in a dataset
forge inspect /path/to/dataset

# Convert it
forge convert /path/to/rlds ./output --format lerobot-v3
forge convert hf://arpitg1304/stack_lego ./stack_lego_rlds --format rlds --workers 4 --visualize
forge convert hf://lerobot/pusht ./pusht_robodm --format robodm

Works with HuggingFace Hub too:

forge inspect hf://lerobot/pusht
forge convert hf://lerobot/pusht ./output --format lerobot-v3

import forge

# Inspect
info = forge.inspect("/path/to/dataset")
print(info.format, info.num_episodes, info.cameras)

# Convert
forge.convert(
    "/path/to/rlds",
    "/path/to/output",
    target_format="lerobot-v3"
)

Automated episode-level quality scoring from proprioception data alone — no video processing needed.

forge quality ./my_dataset
forge quality hf://lerobot/aloha_sim_cube --export report.json

Scores each episode 0-10 based on 8 research-backed metrics:

• Smoothness (LDLJ) — jerk-based smoothness from motor control literature (Hogan & Sternad, 2009)
• Dead actions — zero/constant action detection (Kim et al. "OpenVLA", 2024)
• Gripper chatter — rapid open/close transitions (Sakr et al., 2024)
• Static detection — idle periods where the robot isn't moving (Liu et al. "SCIZOR", 2025)
• Timestamp regularity — dropped frames and frequency jitter
• Action saturation — time spent at hardware limits
• Action entropy — diversity vs repetitiveness (Belkhale et al. "DemInf", 2025)
• Path length — wandering/hesitation in joint space

See forge/quality/README.md for full metric details, paper references, and how to add new metrics.

Filter datasets by quality score, flags, or episode IDs. Supports dry-run previews and pre-computed quality reports.

forge filter ./my_dataset --min-quality 6.0                          # Dry-run preview
forge filter ./my_dataset ./filtered --min-quality 6.0               # Write filtered dataset
forge filter ./my_dataset ./filtered --exclude-flags jerky,mostly_static
forge filter ./my_dataset ./filtered --from-report report.json       # Skip re-analysis

See forge/filter/README.md for full details.

A curated catalog of 23+ prominent robotics datasets — browse, search, and download by name instead of memorizing URIs. Browse the registry online

# Browse all datasets
forge registry list

# Open an interactive HTML browser with filtering
forge registry list --html

# Filter by format, embodiment, or tags
forge registry list --format rlds --embodiment franka
forge registry list --tag manipulation --demo

# Get detailed info on a dataset
forge registry info droid

# Search across names, tags, embodiments, and task types
forge registry search "franka manipulation"

# Validate the registry (for contributors)
forge registry validate

Use dataset IDs directly in any command — no need for full paths or URIs:

forge inspect droid          # resolves to hf://lerobot/droid
forge quality pusht          # resolves to hf://lerobot/pusht
forge convert droid ./output --format lerobot-v3

Download a small demo dataset, inspect it, and run quality scoring — all in one command:

forge demo                   # uses pusht by default
forge demo aloha_sim_cube    # or pick any demo-suitable dataset

See forge/registry/CONTRIBUTING.md for how to add new datasets to the registry.

Automatic episode segmentation via PELT changepoint detection on proprioception signals. Splits episodes into contiguous phases (sub-skills, regime changes, idle periods) without video processing.

forge segment ./my_dataset
forge segment hf://lerobot/droid_100 --export segments.json --plot timeline.png
forge segment ./my_dataset --signal action --penalty bic --cost-model rbf
forge segment ./my_dataset --sample 20

Detects where the statistical properties of the proprio signal change abruptly — e.g., transitions between reaching, grasping, and placing phases. Configurable cost models (rbf, l2, l1), penalty methods (bic, aic, or numeric), and signal selection (observation.state, action, qpos).

See forge/segment/README.md for full details.

See docs/cli.md for the full command reference including:

• forge inspect - Dataset inspection and schema analysis
• forge convert - Format conversion with camera mapping
• forge visualize - Interactive dataset viewer
• forge quality - Episode-level quality scoring (details)
• forge filter - Quality-based episode filtering (details)
• forge registry - Browse and search the dataset registry
• forge demo - Quick-start with a demo dataset
• forge segment - Episode segmentation via changepoint detection (details)
• forge stats - Compute dataset statistics
• forge export-video - Extract camera videos as MP4
• forge hub - Search and download from HuggingFace

For complex conversions, use a YAML config:

forge inspect my_dataset/ --generate-config config.yaml
forge convert my_dataset/ output/ --config config.yaml

See docs/configuration.md for details.

Planned features (contributions welcome!):

• Dataset merging - Combine multiple datasets into one (forge merge ds1/ ds2/ --output combined/)
• Train/val/test splitting - Split datasets with stratification (--split 80/10/10)
• Dataset registry - Curated catalog of 23+ robotics datasets with CLI browser and HTML viewer
• Streaming reads - Process HuggingFace datasets without full download
• Episode filtering - Filter by quality score, flags, or episode IDs (forge filter --min-quality 6.0)
• Depth/point cloud support - Preserve depth streams from RLDS/Open-X
• GR00T writer - Write to NVIDIA Isaac GR00T training format (read support complete)
• Distributed conversion - Scale to 100K+ episode datasets across nodes
• Conversion verification - Automated diff between source and converted data

make venv && source .venv/bin/activate
make install-dev
make test

MIT