Sourcing option
Robot manipulation datasets
Specify tasks, end effectors, actions, and observable outcomes for manipulation learning.
Demonstrations · Actions · Embodied learning
Robotic datasets.
Start with the task.
What does your system observe, what action should it take, and how do you measure success? Those questions shape the dataset before the first episode is collected.
A direct line to Fiund. Source fit and permissions reviewed for your project.
Task-first scope
Specify the outcome your model needs.
Source context
Keep file meaning and lineage connected.
Reviewable quality
Agree on evidence and acceptance criteria.
Direct conversation
Speak with Jaeden at Fiund.
Original photography
Capture environments, in real detail.
Cameras, equipment, and surroundings affect recorded observations. These photographs show our studio workflow; robot embodiments, state signals, and action episodes are specified for the individual collection.
Real source photographs and video frames. Editorial examples; dataset availability and permitted uses are reviewed separately.View sources & full gallery →
Dataset solutions
Find the right source for the model task.
Sourcing option
Visuomotor learning datasets
Align camera observations with interpretable robot actions and state.
Sourcing option
Language-conditioned robot task data
Ground instructions in the actual episode, objects, and completion conditions.
Sourcing option
Cross-embodiment robotics data
Compare robot sources through explicit mappings, retained differences, and testable assumptions.
Sourcing option
Robot task evaluation datasets
Design episodes and labels that support interpretable task-level evaluation.
Category context
Different sources. Different review questions.
Human task demonstrations
Visual examples of manipulation, assembly, and everyday activities. Useful briefs identify viewpoint, task boundaries, objects, and the outcomes the model should recognize.
Robot observation-action data
Episodes tied to a particular robot setup. Specify embodiment, sensor streams, state and action definitions, coordinate conventions, and timing requirements.
Embodied evaluation scenarios
Tasks designed to test behavior under meaningful variations. Describe starting states, allowed actions, completion criteria, failure conditions, and held-out environments.
Why the details matter
Better data starts with a better definition.
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.
Explore quality & provenance →Specification essentials
01Embodiment
02Signals
03Episodes
04Transfer conditions
Technical scope
Specify the collection before counting the volume.
Embodiment
Robot platform, end effector, controller, camera arrangement, and any compatibility constraints.
Signals
Required observations, state variables, action representation, units, coordinate frames, and timestamps.
Episodes
Task instructions, starts and stops, resets, success labels, and failed or partial attempts.
Transfer conditions
Object variation, environment changes, operator variation, and intended deployment differences.
Real recordings
Real human recordings for visual context.
These clips come from our studio production work. They show people and capture conditions, rather than robot control signals or completed robot demonstration collections.
Real conversations at Podcast Studio AZ
Recorded conversations at Podcast Studio AZ · 14 seconds · silent editorial clip.
Jaeden Schafer recording in his podcast studio
Jaeden’s original Riverside recording · 15 seconds · silent editorial clip.
Jaeden Schafer in a real Riverside recording
Jaeden’s original Riverside recording · 15 seconds · silent editorial clip.
How sourcing works
From the first brief to a reviewable handoff.
01 / PROJECT STAGE
Define the target
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.
02 / PROJECT STAGE
Write the specification
Task and target embodiment; Required observation and action signals; Episode counts and success criteria; Sensor timing and annotation requirements; Source permissions and intended use. These details establish the project’s review criteria and reveal where further clarification is needed.
03 / PROJECT STAGE
Review sourcing fit
Discuss which existing sources or collection approaches could address the brief. Identify access constraints, source documentation, and unanswered permission questions before treating a category as usable inventory.
04 / PROJECT STAGE
Inspect representative material
Where appropriate material can be shared, agree on handling and inspect it against the specification. Record defects, coverage gaps, and annotation questions against exact source identifiers.
05 / PROJECT STAGE
Agree the project scope
Confirm the selected material, intended uses, preparation work, acceptance criteria, commercial terms, and delivery arrangements in a project-specific agreement.
06 / PROJECT STAGE
Review the handoff
Check file references, manifests, documented processing, and accepted exceptions. Keep permission review and technical acceptance explicit, and resolve open issues before the collection is treated as approved for the intended use.
Field notes
Practical reading for a sharper brief.
Resource guide
Checking robot embodiment compatibility before sourcing
Compare hardware, sensors, and control assumptions before counting trajectories.
Resource guide
What does a robot action vector actually mean?
Ask for semantics, units, frames, and execution context for every action field.
Resource guide
Joint-space and end-effector action datasets
Clarify which robot representation your project can interpret and use.
Resource guide
Coordinate frames in robot dataset briefs
Make spatial values interpretable across robot, tool, camera, and workspace references.
Resource guide
Units and conventions that belong with robot data
Prevent silent interpretation errors in actions, state, and sensor records.
Resource guide
Reviewing observation-action synchronization
Check which observation a robot command is paired with and how that pairing was created.
Common questions
Before you source.
Is ordinary task video a robot trajectory dataset?
No. Video does not inherently contain robot state, joint values, end-effector commands, or control actions. If you require those signals, name them explicitly so sourcing can be evaluated against the right specification.
Can I inquire about a specific robot platform?
Yes. Include the platform, controller, sensors, action representation, and example schema. Availability and collection feasibility must be discussed before any platform-specific data is promised.
Should failed demonstrations be excluded?
That depends on your task. State whether you need successful demonstrations only, recoveries, negative examples, or a labeled mixture, and define how an outcome is judged.