Real-world manipulation data for autonomy.
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Built for model teams
that need signal, not demos.
Real production context
Collected inside active warehouse and industrial workflows, with natural variance, interruptions, and recovery behaviour that matter for training and evaluation. Not a staged lab environment.
Engineer-ready structure
Episodes include synchronised observations, actions, rewards, dones, per-step metadata, and external JSON. Teams can evaluate format compatibility and ingest data without custom preprocessing.
Rich multi-modal observability
Multi-view RGB and depth, robot pose and kinematics, joint angles, gripper state, wrist force/torque (Fx/Fy/Fz in Newtons per frame), and prompt-conditioned metadata where relevant.
Flexible delivery format
Start with standard datasets for evaluation and benchmarking. Expand into custom collection programmes with defined skills, task structures, specific embodiments, and milestone-based delivery.
Designed for pre-training,
post-training, and evaluation.
Telepath datasets are structured for teams working across the full model development cycle.
Episode containers
.rlog format with observations, actions, rewards, dones, infos, and episode-level metadata. Companion JSON for prompts, tags, and post-processed corrections.
Multi-view visual streams
RGB and depth from multiple viewpoints per episode, covering pick, place, transfer, and recovery sequences under real operating lighting conditions.
Robot state & telemetry
Robot pose (6-DoF ENU frame), joint angles, gripper state (Robotiq 2F-140, Schmalz SBPG, Robotiq ePick), and wrist F/T sensor data per frame.
Prompt-conditioned tasks
Multiskill episodes with conditional inputs and prompt-driven manipulation. Suitable for instruction-following and generalisation benchmarks.
Full schema documentation
Loading workflows, robot setup documentation, coordinate system reference, and data dictionary. No reverse-engineering required on ingestion.
Standard episode
container structure
observations dict[str, array]
actions array[float]
rewards array[float]
dones array[bool]
infos array[dict]
force_torque Fx/Fy/Fz · N/frame
pose 6-DoF · ENU frame
metadata companion .json
Format is consistent across collection programmes. Evaluation samples include full schema and loading scripts on request.
More than one collection
format. More than one use.
Single-arm & dual-arm
Pick, place, and transfer episodes across single and dual-arm setups. Robotiq and Schmalz gripper variants. SKU-conditioned and prompt-conditioned collection.
Humanoid-style setups
Unitree G1 configurations included in the dataset. Suitable for teams working on cross-embodiment transfer or humanoid-specific policy training.
Narrow to long-horizon
From single-skill picks to multiview pick-and-place and multi-step prompt-driven manipulation. Task diversity supports both narrow optimisation and generalisation research.
Data collected in production environments gives model teams access to natural behavioural variance, interruptions, and recovery episodes that matter for training — not only for narrow task optimisation, but for broader generalisation across operating conditions and embodiments.
Need a dataset for a specific embodiment, benchmark, or manipulation capability?
We can start with a sample set, align on technical requirements, and define a collection plan around your training or evaluation goals.