
BUILT FOR TEAMS COLLECTING MANIPULATION DATA AT SCALE
Robot learning teams need more manipulation data, but collection is still too slow, too fragile, and too tied to fixed lab setups. TRumi gives teams a portable, repeatable way to collect human manipulation demonstrations directly in the real world, then turn those recordings into structured datasets through Trossen Data Collection Pipelines

UMI-only data collection
For teams using handheld demonstrations as their primary robot learning data source.

Robot-specific refinement when needed
For workflows that require it, TRumi data can be paired with robot-specific teleoperation or validation on the target hardware.

Bimanual and multi-site collection
Collect more task variation across more operators, objects, and environments.
MADE FOR SCALED
HANDHELD DATA COLLECTION
Every part of TRumi is designed to reduce collection friction, improve repeatability, and keep operators focused on generating useful demonstrations.

Accurate through long sessions
A cam-driven trigger and dual constant-force springs create a smooth 0.4 lb pull, helping reduce finger and grip fatigue during longer collection sessions.

Less downtime, more data
DIY UMI-style builds can fail under load and force teams back into printing, assembly, and repair. TRumi replaces fragile printed structures with a more durable engineered build, so teams spend more time collecting and less time rebuilding.

No re-taping mid-session
Embedded multicolor identifiers are built directly into the finger mounts, reducing reliance on external stickers that can peel, wear, or require rework during collection.

More context per demo
A 177° field of view captures more scene context during each demonstration, compared with 155° on the standard UMI configuration.
DATA WORKFLOW
FROM DEMONSTRATION TO DATASET
TRumi works with Trossen Data Collection Pipelines to turn handheld manipulation demonstrations into structured robot learning datasets.

Perform
Capture single-gripper or bimanual demonstrations with handheld TRumi devices.

Capture
Record wrist-mounted visual observations using the supported GoPro HERO13 Black and Ultra Wide Lens Mod configuration.

Map
Visual-inertial SLAM estimates gripper motion through space.

Extract
Synchronized frames, end-effector pose, and gripper aperture.

Output
Generate structured Zarr or MCAP datasets for use in third-party diffusion-policy training workflows.
End-effector-centric data, not tied to one specific robot joint configuration.
THE UMI IDEA, ENGINEERED FOR SERIOUS DATA COLLECTION
Open-source UMI-style collection proved that handheld manipulation demonstrations can be a powerful robot learning data source. TRumi hardens that concept into a supported product for teams that need repeatable use, cleaner operation, and scalable deployment.
UMI

ZERO BACKLASH
CAM DRIVE
Cam-driven motion supported by dual precision linear rails for a smoother, more repeatable operator feel.
3D PRINTED
GEARS
Gear-driven triggers introduce play and wear, and the printed parts are the first thing to fail under load.


EMBEDDED
IDENTIFIERS
Built directly into the finger mounts. Nothing to peel, wear off, or re-apply mid-session.
STICKER
IDENTIFIERS
Reflective tape and stickers wear off with handling and need re-application and recalibration.


CONSTANT-FORCE
SPRINGS
Constant-force springs deliver the same smooth resistance through the entire trigger pull.
COMPRESSION
SPRING
Compression springs fight back harder the further you squeeze.

Feature | DIY / Open-source UMI-style build | TRumi | High-end handheld systems |
|---|---|---|---|
Trigger mechanism | 3D-printed gears, backlash | Cam drive, zero backlash | Precise but over-engineered |
Durability | Fragile, reprints needed | CNC-machined metal components, designed for serviceability | Durable but heavy/costly |
Setup | Manual assembly | Ready out of the box | Heavier integration |
Identifiers | External stickers (wear off) | Embedded, multicolor | Varies |
Field of view | 155° | 177° | Varies |
Trigger mechanism
TRumi
Cam drive, zero backlash
DIY / Open-source UMI-style build
3D-printed gears, backlash
High-end handheld systems
Precise but over-engineered
Durability
TRumi
CNC-machined metal components, designed for serviceability
DIY / Open-source UMI-style build
Fragile, reprints needed
High-end handheld systems
Durable but heavy/costly
Setup
TRumi
Ready out of the box
DIY / Open-source UMI-style build
Manual assembly + tape
High-end handheld systems
Heavier integration
Identifiers
TRumi
Embedded, multicolor
DIY / Open-source UMI-style build
External stickers (wear off)
High-end handheld systems
Varies
Field of view
TRumi
Embedded, multicolor
DIY / Open-source UMI-style build
External stickers (wear off)
High-end handheld systems
Varies
Cost profile
TRumi
Practical middle ground
DIY / Open-source UMI-style build
$400-500 printing cost, higher build and maintenance burden
High-end handheld systems
$10K-15K for a pair
CHOOSE YOUR TRUMI KIT
For academic or volume discounts, request a custom quote.
FREQUENTLY ASKED QUESTIONS
What is TRumi?
TRumi is Trossen Robotics’ handheld manipulation data collection system for collecting robot learning demonstrations quickly, intuitively, and with minimal setup.
Is TRumi only for Trossen robots?
No. TRumi data is centered around end-effector motion rather than a specific robot’s joint configuration. That said, deployment on a target robot still requires integration and validation.
Does TRumi replace leader-follower teleoperation?
Not exactly. TRumi can be used as a primary handheld data collection modality. It can also be paired with leader-follower teleoperation when teams need final embodiment-specific tuning or validation on a target robot.
What outputs does TRumi support at launch?
TRumi outputs structured Zarr or MCAP datasets through Trossen Data Collection Pipelines.
What camera setup is supported?
The intended launch configuration uses the GoPro HERO13 Black with the Ultra Wide Lens Mod.
What is TRumi not ideal for?
TRumi is not the primary tool for highest-precision robot-specific refinement or final validation on target robot hardware. It is best for scalable handheld manipulation demonstrations, broader task variation, and dataset generation.
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