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Browse a curated catalog of datasets for humanoid robots and embodied AI, spanning real-world demonstrations, teleoperation, motion capture, egocentric vision, simulation, manipulation, locomotion, and cross-embodiment robot learning.

83 Datasets · Page 2 of 7

RoboArena distributed pairwise robot policy evaluation workflow

RoboArena

RoboArena is a distributed real-world evaluation dataset for generalist policies on the DROID robot platform. The July 17, 2026 snapshot contains 3,883 evaluation sessions and 10,783 autonomous policy episodes, with 27,148 multi-view videos, matching proprioception and action files, task instructions, success scores, pairwise preferences, and evaluator feedback. Double-blind comparisons let participating institutions choose their own tasks and environments while preserving matched conditions within each policy comparison.

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AIRoA MoMa household tasks performed by Human Support Robots

AIRoA MoMa Dataset

AIRoA MoMa v1.1 provides 23,762 filtered Human Support Robot teleoperation episodes totaling 87 hours across seven household task families. The gated LeRobot release pairs head- and hand-camera RGB video with robot state, calibration, task-success metadata, and hierarchical short-horizon and primitive-action annotations; the research dataset also records synchronized wrist force-torque signals. Nineteen operators used eight HSR robots to collect tasks including towel handling, coffee making, dishwashing, toast preparation, lamp control, and slipper organization.

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MolmoBot simulated and real robot manipulation examples

MolmoBot-Data

MolmoBot-Data contains 1.7 million procedurally generated expert trajectories totaling 5,704 hours across eight manipulation task types on Franka and RB-Y1 platforms. MolmoBot-Engine uses task-and-motion planning and randomizes objects, receptacles, lighting, and camera poses across more than 94,000 simulated indoor environments. The data supports articulated-object interaction, pick-and-place, and mobile manipulation research, including zero-shot sim-to-real policy training.

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Top-camera view of a MolmoAct2 Bimanual YAM manipulation scene

MolmoAct2 Bimanual YAM Dataset

MolmoAct2 Bimanual YAM is a large-scale collection of real bimanual manipulation demonstrations gathered on the custom Yet Another Manipulator platform. The merged LeRobot release contains 32,246 episodes, 76 million frames, and 34 language-annotated tasks with top, left, and right RGB views plus 14-dimensional joint state and action data; the full collection is described as more than 720 hours of training data. Tasks range from folding clothes and untangling cables to scanning groceries, packing medication, and bussing tables.

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Top-camera view from a MolmoAct2 SO-100 or SO-101 robot setup

MolmoAct2 SO-100/SO-101 Dataset

MolmoAct2 SO-100/SO-101 curates 38,059 demonstrations, 19.8 million frames, and about 184 hours from more than 1,200 public LeRobot repositories contributed by 377 users. A four-stage pipeline checks structural validity, excludes evaluation data, enforces source eligibility, and applies a TOPReward quality gate while retaining varied tasks, camera setups, objects, environments, and both low-cost arm embodiments. The release also supplies per-episode language annotations for the source datasets.

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FTP-1 dataset sources, tactile sensors, and robot embodiments

FTP-1 Dataset

FTP-1 Dataset aggregates about 3,000 hours of tactile manipulation data from 26 human and robot sources across 21 sensors. It spans image-based, array-based, and state-based touch on dexterous hands and gripper robots, with tactile annotations normalized through the Morphology-Aware Tactile Token Space and instructions rewritten for linguistic diversity. The mixture is designed to pretrain transferable contact-rich manipulation policies across sensors and embodiments.

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LESS tactile palpation and internal-structure reconstruction

LESS (Local Encoder for Spatial Sensing)

The LESS release contains more than 800 hours of tactile palpation measurements for reconstructing the internal structure of soft breast phantoms. A Franka Panda with a gel-based tactile sensor collected controlled poke and motion-primitive trajectories, while additional sets cover larger phantoms, multiple inclusions, and hand-held sensing. Force and pose sequences are paired with MRI-derived geometry for training and evaluating local 2D and 3D tactile imaging models.

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RoboDojo's unified simulation and real-world manipulation benchmark

RoboDojo

RoboDojo is an evaluation-focused benchmark spanning 42 Isaac Sim tasks and 18 real-world manipulation tasks across three robot embodiments. Its tasks probe generalization, memory, precision, long-horizon execution, and open-vocabulary instruction following, while the release provides benchmark assets, configuration validation, seed-controlled layouts, and result artifacts. RoboDojo-RealEval standardizes hardware, scene resets, evaluation protocols, and deployment interfaces for reproducible physical testing.

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A Unitree G1 performing whole-body household manipulation tasks

AgiBot-to-Unitree G1 Retarget / TrajBooster

TrajBooster's approximately 35-hour, 30 GB dataset contains 1,960 episodes across 176 AgiBot-World tasks retargeted from a wheeled humanoid to Unitree G1. It extracts dual-arm 6D end-effector trajectories from real source data, tracks them with a whole-body controller in Isaac Gym, and replaces source actuator commands with G1-compatible actions while preserving source vision and language, producing heterogeneous vision-language-action triplets. The data supports cross-embodiment VLA post-pre-training before adaptation with about ten minutes of target-robot teleoperation.

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