truelabelRequest data

Robot action data

Teleoperation data marketplace

A teleoperation data marketplace lets robotics teams source synchronized camera streams, joint states, end-effector poses, action traces, task labels, and success/failure metadata captured while a human operator controls the robot. Truelabel matches teleop sourcing requests to vetted capture partners and routes samples through buyer review before scale.

Updated 2026-05-05
By truelabel
Reviewed by truelabel ·
teleoperation data marketplace
Request teleoperation dataHow sourcing works

Quick facts

Robot state
Joint positions, velocities, end-effector pose
Video
Synced wrist, egocentric, or external camera streams
Task
Pick, place, open, close, sort, assemble, recover
Format
MCAP, HDF5, RLDS, LeRobot, or buyer-defined schema
QA
Sync tolerance, completed task segments, metadata completeness

Comparison

Data typeContainsBest for
Egocentric videoHuman POV footageWorld-model and perception pretraining
Robot demonstrationsHuman task examplesImitation and behavior cloning
Teleoperation dataRobot actions and synchronized observationsPolicy learning and VLA fine-tuning

Provider list — Teleoperation data marketplace

10 providers covering teleoperation data marketplace. Each entry summarizes the provider's strongest fit and a buyer-bottleneck signal so you can shortcut the discovery loop.

  1. #1

    Open X-Embodiment

    22-dataset cross-embodiment teleoperation/manipulation corpus across 21 institutions, ~22 robots, ~1M trajectories.

    Best for: Cross-embodiment pretraining when your policy needs exposure to many robot platforms.

  2. #2

    DROID

    76k teleoperated Franka demonstrations across 564 scenes, 13 institutions, with synchronized observations and action streams.

    Best for: Real-world Franka manipulation; richest single dataset for in-the-wild teleop.

  3. #3

    BridgeData V2

    60,096 teleoperated trajectories across 24 environments, primarily WidowX-arm tabletop tasks.

    Best for: Imitation-learning baselines on tabletop tasks with permissive research license.

  4. #4

    RoboTurk

    Crowdsourced teleoperation pipeline from Stanford with public datasets focused on bin-picking and assembly tasks.

    Best for: Reference architecture for crowdsourced teleop and a workhorse benchmark for early-stage policy work.

  5. #5

    Mobile ALOHA

    Stanford bimanual mobile-manipulation platform with public demonstration datasets and open hardware.

    Best for: Bimanual + mobile teleop, replicable hardware, strong open-source ecosystem.

  6. #6

    RoboCasa

    Large-scale simulation framework with kitchen scene diversity and teleoperated demonstration support.

    Best for: Sim-first teleoperation augmentation when scene/object diversity is the bottleneck.

  7. #7

    NVIDIA Isaac Sim teleop

    NVIDIA's robotics simulation platform with teleop-friendly extensions and Cosmos integration for synthetic-real bridging.

    Best for: Sim-real teleop pipelines where physics-grounded environments + Omniverse integration matter.

  8. #8

    Hugging Face LeRobot

    Open robotics framework + dataset hub from Hugging Face with multiple teleop benchmarks (PushT, ALOHA, xArm).

    Best for: Modern open-source teleop ingestion path with Parquet + video observation conventions.

  9. #9

    Scale AI

    Managed labeling and capture operations including teleoperation segments for autonomous vehicle and robotics customers.

    Best for: Enterprise managed teleop programs with single-vendor accountability.

  10. #10

    RT-1 / RT-2 datasets

    Google DeepMind's RT-1/RT-2 models trained on diverse manipulation data spanning 13 robots and 17 months.

    Best for: Reference data composition for VLA-style policies trained on teleop demonstrations.

What makes teleop data useful

Useful teleoperation data is more than a video export. It preserves synchronized observations and actions [1], explicit episode and step boundaries [2], and timestamped multimodal logs [3] so buyers can audit whether each accepted sample can train or evaluate policies.

"Overall we have 30,050 trajectories in the dataset, out of which 9,500 are collected through teleoperation."

[4]

That public dataset pattern is the minimum bar for a marketplace spec: ask for trajectory counts, camera viewpoints, task and scene coverage, and failure labels before funding scale-up [5].

How truelabel routes teleop sourcing requests

The sourcing request captures robot embodiment, teleoperation interface, sensor package, delivery format, and acceptance criteria. truelabel routes suppliers according to whether their rigs can export policy-ready action data [6], whether the proposed collection fits real-world deployment environments [7], and whether the capture partner can support physical-AI data operations rather than generic annotation [8]. Vetted suppliers respond only when their rigs and exports match the buyer's capability vector.

Related pages

Use these to move from category-level context into specific task, dataset, format, and comparison detail.

Best teleoperation data providers 2026Related pageTeleoperation data vs robot demonstration dataRelated pageSourcing teleop kitchen dataRelated pageSourcing teleop warehouse dataRelated pageTeleoperation training dataTask-specific requirementsBest robotics dataset marketplaces 2026Related pageRobot training data marketplaceBuyer conversion pageSourcing mocap human demonstrationsRelated page

External references and source context

  1. Project site

    Teleop data should pair synchronized observations and robot actions for policy learning.

    droid-dataset.github.io ↩
  2. RLDS: Reinforcement Learning Datasets

    Teleop specs should define episodes, steps, observations, actions, and metadata.

    GitHub ↩
  3. MCAP file format

    MCAP stores timestamped multimodal robotics logs for delivery and replay.

    mcap.dev ↩
  4. Dataset page

    RoboSet reports 9.5 thousand teleoperated trajectories.

    robopen.github.io ↩
  5. Teleoperation datasets are becoming the highest-intent physical AI content category

    Teleop sourcing requests should specify embodiment, interface, cameras, rate, and success bar.

    tonyzhaozh.github.io ↩
  6. Project site

    Robot policies benefit from action data paired with observations across tasks and embodiments.

    robotics-transformer-x.github.io ↩
  7. Figure + Brookfield humanoid pretraining dataset partnership

    Commercial humanoid teams pursue real-world training data from deployment environments.

    figure.ai ↩
  8. scale.com physical ai

    Physical AI vendors route custom robotics data collection and data-engine workflows.

    scale.com ↩

FAQ

What is teleoperation data?

Teleoperation data is data recorded while a human remotely controls a robot. It usually includes robot state, actions, camera observations, timestamps, and task metadata that can train or evaluate robot policies.

What formats can teleoperation data use?

Common formats include MCAP, HDF5, RLDS, LeRobot datasets, ROS bag exports, JSON, CSV, and buyer-specific schemas. The sourcing request should define the required format before suppliers submit samples.

How much teleoperation data should I request?

The right volume depends on the task, robot embodiment, success criteria, and model architecture. A small eval request can validate sample quality before the buyer funds a larger capture program.

Can teleop data be exclusive?

Yes. Net-new teleop sourcing requests can specify exclusive rights. Off-the-shelf datasets are typically non-exclusive unless the buyer pays for exclusivity.

Looking for teleoperation data marketplace?

Specify modality, task, environment, rights, and delivery format. Truelabel matches you with vetted capture partners — every delivery includes consent artifacts and commercial licensing by default.

Request teleoperation data