datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
FoldingTShirt_DualArxR5a_Samples
FoldingTShirt_DualArxR5a_Samples
100 real-robot teleoperation episodes for “Fold the T-shirt on the table.” on a DualArxR5a dual-arm robot. Format: raw MCAP (ROS 2 / rosbag2).
Source
Collected with TeleXperience, IO-AI’s product for real-robot teleoperation and data collection. An operator drives the robot; TeleXperience writes time-aligned RGB, joint commands, joint states, gripper targets, and end-effector poses to MCAP.
Product page:… See the full description on the dataset page: https://huggingface.co/datasets/io-intelligence/FoldingTShirt_DualArxR5a_Samples.broken_old_PR_rebot_mini_towel_folding_annotatedThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 30,
"features": {
"action": {
"dtype": "float32",
"names": [
"shoulder_pan.pos",
"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_yaw.pos",
"wrist_roll.pos",
"gripper.pos"
]… See the full description on the dataset page: https://huggingface.co/datasets/nikodembartnik/broken_old_PR_rebot_mini_towel_folding_annotated.trlc_tshirt_folding_robometer_laundromat_fold_20260314
Robometer Annotations Dataset
This dataset contains one Robometer annotation bundle per episode.
Files
episodes/episode_000000.npz: Per-episode arrays aligned to the original frame indices
episodes.parquet: One manifest row per episode, including episode_index
episodes.jsonl: The same manifest in JSONL format
run_metadata.json: Run-level metadata and export settings
Usage
import numpy as np
import pandas as pd
from huggingface_hub import hf_hub_download… See the full description on the dataset page: https://huggingface.co/datasets/djkesu/trlc_tshirt_folding_robometer_laundromat_fold_20260314.tshirt-folding
T-Shirt Folding Mixed Manifest
This repo is a lightweight manifest for a public T-shirt folding collection built from five source datasets:
Gongsta/trlc_tshirt_folding
Gongsta/trlc_tshirt_folding_impedance
Gongsta/dagger_dk1_tshirt_corrections
Gongsta/krish-simpler-tshirt
Gongsta/e7-tshirt-folding
The goal is to provide one clean public entrypoint while preserving each source dataset at its highest native frame rate.
Project Background
This dataset card was created… See the full description on the dataset page: https://huggingface.co/datasets/djkesu/tshirt-folding.cloth_folding_positive_20250720_161717wan_folding_paperThis dataset contains videos generated using Wan 2.1 T2V 14B.
trlc_tshirt_folding_robometer_laundromat_fold_20260314_fixed128
Robometer Annotations Dataset
This dataset contains one Robometer annotation bundle per episode.
Files
episodes/episode_000000.npz: Per-episode arrays aligned to the original frame indices
episodes.parquet: One manifest row per episode, including episode_index
episodes.jsonl: The same manifest in JSONL format
run_metadata.json: Run-level metadata and export settings
Usage
import numpy as np
import pandas as pd
from huggingface_hub import hf_hub_download
#… See the full description on the dataset page: https://huggingface.co/datasets/djkesu/trlc_tshirt_folding_robometer_laundromat_fold_20260314_fixed128.alphafold-folding-trajectory-functional-stability-coherence-v0.1What this dataset tests
Whether predicted folding trajectories
remain predictive of functional stability
under stress conditions.
Structure alone is not enough.
The path to structure must stay coherent
with real-world stability.
When that relationship breaks
therapeutic proteins fail
in storage
manufacturing
or use.
Required outputs
trajectory_coherence_score
stability_divergence_flag
degradation_horizon_hours
critical_structure_region
stabilization_strategy
Use case
Antibody engineering… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/alphafold-folding-trajectory-functional-stability-coherence-v0.1.Protein_Folding_Stability_Predprotein-folding-instability-trajectory-benchmark-v0.2Protein Folding Instability Trajectory Benchmark v0.2
Overview
This benchmark evaluates whether models can detect protein folding instability trajectories.
Unlike many protein AI tasks, the objective here is not to predict the final folded structure.
Instead the model must determine whether a folding trajectory is moving toward:
stable folding convergence
or
future misfold instability.
Protein folding occurs within an energy landscape containing multiple basins.
A folding process may converge… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/protein-folding-instability-trajectory-benchmark-v0.2.folding_nets_vissim_testfolding_netsfolding_nets_3d_perception_testfolding_netsfolding_nets_VSimprotein-folding-pathway-instability-v0.1
protein-folding-pathway-instability-v0.1
What this dataset does
This dataset evaluates whether models can detect instability in protein folding pathways.
Each row represents a simplified protein folding scenario defined by structural and interaction proxies.
The task is to determine whether the folding pathway is stable or likely to produce misfolding or aggregation.
Core stability idea
Protein folding stability depends on interactions between:
hydrophobic… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/protein-folding-pathway-instability-v0.1.folding_nets_testfolding_nets_2d_perception_testcloth_folding_negative_20250720_161717protein-folding-misfold-risk-mini-v0.1Protein Folding Misfold Risk Mini Benchmark v0.1
Overview
This benchmark evaluates whether models can detect folding trajectories likely to drift toward protein misfold or instability.
The dataset intentionally avoids predicting final protein structure.
Instead it focuses on identifying dynamic folding instability.
Protein folding is commonly described as movement through an energy landscape containing multiple basins.
A protein may converge toward the correct folded structure or fall into a… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/protein-folding-misfold-risk-mini-v0.1.