# Robotic Datasets

> Scope robotic and embodied AI dataset sourcing, from human task video to synchronized robot demonstrations. Discuss observations, actions, sensors, and licensing with Fiund.

Robotics data can mean human demonstrations, robot trajectories, observations paired with actions, or recordings of interactions with objects and environments. These are related but not interchangeable. Robotic Datasets helps Fiund buyers specify which signals their training or evaluation project actually requires.

Operated by Fiund. Contact: jaeden@fiund.com. Source fit, inventory, permissions, pricing, and delivery require project-specific review.

## Pages

- [Robotic Datasets | Dataset sourcing by Fiund](https://roboticdatasets.com/): Scope robotic and embodied AI dataset sourcing, from human task video to synchronized robot demonstrations. Discuss observations, actions, sensors, and licensing with Fiund.
- [Real photography and recordings | Robotic Datasets](https://roboticdatasets.com/media/): Real studio photographs, recording frames, video clips, and source notes behind Robotic Datasets.
- [Dataset directory | Robotic Datasets](https://roboticdatasets.com/datasets/): Browse robotic datasets sourcing options, project specifications, and collection review questions with Fiund.
- [Dataset solutions | Robotic Datasets](https://roboticdatasets.com/solutions/): Explore task-specific robotic datasets sourcing specifications.
- [Resource library | Robotic Datasets](https://roboticdatasets.com/resources/): Practical guides to source selection, preparation, annotation, and review for robotic datasets.
- [Project pricing | Robotic Datasets](https://roboticdatasets.com/pricing/): Quote factors, project scope, and commercial review for robotic datasets.
- [Quality and provenance | Robotic Datasets](https://roboticdatasets.com/quality/): Review source lineage, technical fit, annotations, and project permissions for robotic datasets.
- [Discuss a dataset project | Robotic Datasets](https://roboticdatasets.com/contact/): Send your robotic datasets project brief directly to Jaeden at Fiund.
- [How dataset sourcing works | Robotic Datasets](https://roboticdatasets.com/how-it-works/): From a robotic datasets project brief to a defined specification and reviewable delivery.
- [About this Fiund sourcing site | Robotic Datasets](https://roboticdatasets.com/about/): Meet the purpose and contact behind Robotic Datasets.
- [For data owners | Robotic Datasets](https://roboticdatasets.com/for-data-owners/): Introduce a robotic datasets archive to Fiund for project-fit review.
- [Privacy and inquiries | Robotic Datasets](https://roboticdatasets.com/privacy/): Browsing, local brief building, and email inquiry information for Robotic Datasets.
- [Website information and project scope | Robotic Datasets](https://roboticdatasets.com/website-terms/): The role of Robotic Datasets guides, sourcing descriptions, and project-specific agreements.
- [Robot manipulation datasets | Robotic Datasets](https://roboticdatasets.com/solutions/robot-manipulation-data/): Specify tasks, end effectors, actions, and observable outcomes for manipulation learning.
- [Visuomotor learning datasets | Robotic Datasets](https://roboticdatasets.com/solutions/visuomotor-learning-data/): Align camera observations with interpretable robot actions and state.
- [Language-conditioned robot task data | Robotic Datasets](https://roboticdatasets.com/solutions/language-conditioned-robot-tasks/): Ground instructions in the actual episode, objects, and completion conditions.
- [Cross-embodiment robotics data | Robotic Datasets](https://roboticdatasets.com/solutions/cross-embodiment-datasets/): Compare robot sources through explicit mappings, retained differences, and testable assumptions.
- [Robot task evaluation datasets | Robotic Datasets](https://roboticdatasets.com/solutions/robot-task-evaluation-data/): Design episodes and labels that support interpretable task-level evaluation.
- [Checking robot embodiment compatibility before sourcing | Robotic Datasets](https://roboticdatasets.com/resources/robot-embodiment-compatibility/): Compare hardware, sensors, and control assumptions before counting trajectories.
- [What does a robot action vector actually mean? | Robotic Datasets](https://roboticdatasets.com/resources/robot-action-representation/): Ask for semantics, units, frames, and execution context for every action field.
- [Joint-space and end-effector action datasets | Robotic Datasets](https://roboticdatasets.com/resources/joint-space-vs-end-effector-data/): Clarify which robot representation your project can interpret and use.
- [Coordinate frames in robot dataset briefs | Robotic Datasets](https://roboticdatasets.com/resources/robot-coordinate-frames/): Make spatial values interpretable across robot, tool, camera, and workspace references.
- [Units and conventions that belong with robot data | Robotic Datasets](https://roboticdatasets.com/resources/units-and-conventions-robot-data/): Prevent silent interpretation errors in actions, state, and sensor records.
- [Reviewing observation-action synchronization | Robotic Datasets](https://roboticdatasets.com/resources/observation-action-synchronization/): Check which observation a robot command is paired with and how that pairing was created.
- [Camera calibration and setup metadata for robot datasets | Robotic Datasets](https://roboticdatasets.com/resources/robot-camera-calibration-metadata/): Request geometry information according to the model task and source configuration.
- [Episode boundaries and resets in robot datasets | Robotic Datasets](https://roboticdatasets.com/resources/episode-boundaries-and-resets/): Keep attempts, setup, interruptions, and terminal conditions distinguishable.
- [Defining success and failure in robot task data | Robotic Datasets](https://roboticdatasets.com/resources/robot-success-failure-labels/): Distinguish final outcome, procedural correctness, and observable evidence.
- [Recovery demonstrations and retries in robot data | Robotic Datasets](https://roboticdatasets.com/resources/recovery-demonstrations-robotics/): Preserve context around errors rather than retaining only final successful motion.
- [What to ask about teleoperated robot demonstrations | Robotic Datasets](https://roboticdatasets.com/resources/teleoperation-dataset-metadata/): Describe operator inputs, robot commands, control modes, and interventions.
- [Understanding gripper fields in robot datasets | Robotic Datasets](https://roboticdatasets.com/resources/gripper-command-semantics/): Clarify opening, closing, targets, feedback, and grasp evidence.
- [Multi-camera observations in robot learning datasets | Robotic Datasets](https://roboticdatasets.com/resources/multi-camera-robot-observations/): Define camera roles, stream identity, timing, and missing-view behavior.
- [Grounding language instructions in robot episodes | Robotic Datasets](https://roboticdatasets.com/resources/grounding-language-in-robot-episodes/): Connect text to the requested task without confusing it with execution evidence.
- [Object variation in manipulation datasets | Robotic Datasets](https://roboticdatasets.com/resources/object-variation-robot-manipulation/): Specify instances, properties, and task relationships that matter to the robot.
- [Workspace and layout variation for robot datasets | Robotic Datasets](https://roboticdatasets.com/resources/workspace-layout-generalization/): Describe initial conditions and environment changes beyond a location label.
- [Action horizons and sequence sampling in robot data | Robotic Datasets](https://roboticdatasets.com/resources/action-horizons-and-sequence-sampling/): Keep recorded steps, training windows, and execution assumptions distinct.
- [Sampling rates and missing records in robot datasets | Robotic Datasets](https://roboticdatasets.com/resources/sampling-rates-and-missing-robot-data/): Distinguish fresh measurements, resampled values, padding, and genuine inactivity.
- [Reviewing sensor integrity in robot episodes | Robotic Datasets](https://roboticdatasets.com/resources/robot-sensor-integrity-review/): Check values, stream continuity, and task consistency before assessing model fit.
- [Where human task video fits in robot learning data | Robotic Datasets](https://roboticdatasets.com/resources/human-video-and-robot-policy-data/): Keep semantic demonstrations distinct from robot observations and action records.
- [Combining robot datasets without losing source meaning | Robotic Datasets](https://roboticdatasets.com/resources/combining-multiple-robot-datasets/): Standardize carefully while preserving embodiment, conventions, and provenance.
- [Designing train and evaluation splits for robot datasets | Robotic Datasets](https://roboticdatasets.com/resources/robot-train-test-split-design/): Choose grouping rules that match the actual generalization question.
- [Schemas and versions for robot dataset deliveries | Robotic Datasets](https://roboticdatasets.com/resources/robot-dataset-schema-versioning/): Keep field definitions, episode structure, and revisions traceable.
- [A practical pilot review for robot datasets | Robotic Datasets](https://roboticdatasets.com/resources/robot-data-pilot-acceptance/): Resolve embodiment, timing, action meaning, and task evidence before expansion.
- [Provenance questions for robot dataset sourcing | Robotic Datasets](https://roboticdatasets.com/resources/robot-dataset-provenance-review/): Keep collection history, transformations, and proposed use review attached to the data.
- [Specify a robot demonstration dataset | Robotic Datasets](https://roboticdatasets.com/robot-demonstration-dataset-brief/): Define embodiment, observations, actions, timing, and episode outcomes before sourcing.
- [Human demonstration video versus robot action data | Robotic Datasets](https://roboticdatasets.com/human-video-vs-robot-action-data/): Understand which information each data type can contribute to an embodied AI project.

- [Complete site text](https://roboticdatasets.com/llms-full.txt)