HaptalAI/so100-curated

HaptalAI / SO-100 Curated Manipulation Pack

A quality-filtered, failure-labelled merge of SO-100 community manipulation datasets. Built for imitation learning, RL from demonstrations, and failure-aware training.

What This Is

The SO-100 open-source robot arm has generated dozens of small community datasets on HuggingFace, each contributed by independent researchers and hobbyists. Individually these datasets are too small and inconsistent to train on reliably. This pack:

• Merges 13 community SO-100/SO-101 datasets into a single parquet file
• Classifies every episode as CLEAN (47) or FAILURE (48); removes CORRUPTED episodes (1) entirely
• Keeps failure episodes — labelled but not discarded — because failure data is valuable for reinforcement learning and anomaly-aware imitation
• Provides full provenance via source_dataset and original_episode_id columns

Curation Methodology

Why We Keep Failure Episodes

Most curated datasets silently discard failures. We believe this is a mistake:

• RL from demonstrations needs to distinguish successful from failed rollouts
• Failure-conditioned imitation (DAgger, HG-DAgger) requires negative examples
• Anomaly detection for safety systems needs real failure signatures
• Contrastive training — failure episodes teach the policy what not to do

Failures are flagged with use_for_training=false and a failure_type tag so downstream users can include or exclude them with a single filter.

Classification Rules

All classification streams parquet data only — no video is downloaded.

Label	Condition	Action
CORRUPTED	NaN/Inf >50% frames; all channels frozen (std=0); empty episode	Removed entirely
FAILURE	Velocity spike (z-score > 6.5); action-state mismatch (>50% frames off by >20% of range)	Kept, use_for_training=false
CLEAN	Passes all checks	Kept, use_for_training=true

Episode boundaries inferred from episode_index column; fallback to fixed 200-frame windows.

Column Schema

Column	Type	Description
source_dataset	string	HuggingFace dataset ID this episode came from
original_episode_id	string	Episode key as it appeared in the source dataset
frame_index	int32	0-based timestep index within the episode
use_for_training	bool	true = clean; false = failure (keep for RL)
failure_type	string	none | velocity_spike | action_state_mismatch
quality_score	float32	Quality score [0,1]; 1.0 = perfect
episode_length	int32	Total frames in this episode (same for all rows of an episode)
state_0 … state_6	float64	Joint position / observation state (7 dims for SO-100)
action_0 … action_6	float64	Commanded action targets (7 dims for SO-100)

Layout: one row per timestep. Episode metadata (use_for_training, failure_type, etc.) is repeated on every row, enabling direct pandas filtering without joins.

How to Load

from datasets import load_dataset
import pandas as pd, numpy as np

# Load everything (one row = one timestep)
ds = load_dataset("HaptalAI/so100-curated", split="train")
df = ds.to_pandas()

# Filter: only clean timesteps for BC / imitation learning
clean_df = df[df["use_for_training"] == True]

# Filter: only failure timesteps for RL / anomaly work
fail_df = df[df["use_for_training"] == False]

# Reconstruct one full episode as a trajectory tensor
ep_df = df[(df["source_dataset"] == "cadene/so100_test") &
           (df["original_episode_id"] == "ep_0000")]
state  = ep_df[[f'state_{i}'  for i in range(7)]].to_numpy()   # (T, 7)
action = ep_df[[f'action_{i}' for i in range(7)]].to_numpy()   # (T, 7)

# Or load directly from parquet
df = pd.read_parquet("episodes.parquet")

Stats

Metric	Value
Source datasets	13
Total episodes kept	95
Clean episodes (use_for_training=true)	47
Failure episodes (use_for_training=false)	48
Corrupted episodes removed	1
Total timestep rows in parquet	49,073
Parquet file size	0.8 MB
Generated	2026-05-24

Attribution

See sources.md for the full per-dataset breakdown with episode counts, authors, and licenses.

All original data remains under its original license. This curated pack adds quality labels and is released under Apache 2.0.

Limitations

• Episode classification is heuristic — some edge cases may be mislabelled
• Trajectory columns vary by source; not all episodes have all columns
• Video observations are not included (parquet-only streaming)
• Gated / authentication-required datasets are excluded
• This is a community effort; verify episodes before production training

Contact

Questions, corrections, or additional source datasets: aarav@haptal.ai

Built with ❤️ for the open-source robotics community.