Blomega Alternatives: RLHF Tooling vs Physical AI Data Capture

Quick facts

Topic

Blomega

Audience

Procurement leads, ML ops, robotics engineers

Deliverable

Buyer-facing reference + procurement guidance

What Blomega and Blolabel actually do

Blomega Lab is a Nevada-based AI development company, roughly three years old, that lists data annotation, real-time translation, and model evaluation as services. Its one shipped product is Blolabel, a mobile app from Blomega LLC that runs RLHF annotation: labelers rank model outputs and flag bad completions to fine-tune language models. Public documentation stops at a company profile, an app-store listing, and a single blog post. There is no API reference, no pricing, no SLA, and no case study.

That blog post is also the only performance number Blomega publishes: a claimed 40 percent cut in RLHF operating cost with no loss in annotator agreement. For a text and image labeling tool, that is a reasonable pitch. For robotics it misses the point, because nothing on Blomega's public surfaces mentions RLDS, sensor rigs, teleoperation, or the multi-sensor capture that embodied models train on. If your data lives in the physical world, Blolabel is not a cheaper version of what you need. It is a different tool.

Why RLHF annotation is not physical-AI capture

RLHF and physical-AI data solve different problems, and no amount of app polish closes the gap. Blolabel's job is judgment: a person reads two model answers and picks the better one. Nothing is instrumented. Physical-AI training needs the opposite, tightly synchronized sensor streams recorded while a body acts in the world. RT-1 trained on 130,000 robot demonstrations across 700+ tasks, each pairing RGB frames with proprioceptive joint states and end-effector poses at 3 Hz or faster^[1]. DROID collected 76,000 teleoperation trajectories on Franka arms with wrist-mounted and third-person cameras^[2]. A phone labeling app produces none of that.

The moment your pipeline needs depth, contact forces, or time-aligned action labels, an annotation vendor is the wrong category rather than a cheaper one. This is why Scale AI's Universal Robots work and LeRobot's HDF5 schema specify hardware, calibration, and episode structure up front: a policy is only as trustworthy as the sensor sync behind its training set.

Blomega vs Truelabel: side-by-side

The two platforms barely overlap. This is where each one actually operates.

How Truelabel captures data that does not exist yet

Truelabel is a two-sided marketplace, not a labeling queue. Around 10,000 vetted collectors across 100 countries own the rigs (RealSense depth cameras, Franka arms, wearable IMUs) and record demonstrations to a buyer's spec. Post a bounty for 10,000 warehouse-navigation runs across varied lighting, floor textures, and obstacle densities, and collectors capture episodes that are simply not in RoboNet or BridgeData. That distributed base also makes domain randomization close to free: one bounty can span dozens of physical locations, the environmental spread that domain randomization and sim-to-real work links to better generalization.

Every dataset ships with provenance records (capture hardware, calibration certificates, collector consent) in HDF5, MCAP, or RLDS, matching the inputs OpenVLA and LeRobot expect. Enrichment (bounding boxes, grasp affordances, failure-mode labels, trajectory segmentation) is done by domain experts, not crowd workers. For RT-2-style vision-language-action models, collectors narrate each step, so you get paired observation, language, and action tuples instead of raw video.

From spec to training-ready dataset

The pipeline runs in four stages, and each one is gated before the next begins.

1. 01

   Scope the bounty

   Specify task domain, sensor modalities (RGB-D, IMU, force-torque), episode count, delivery format, and what counts as an accepted clip.

2. 02

   Capture in the field

   Collectors with matching rigs record in the target environment. The app runs calibration and sync checks, and each clip carries camera intrinsics, IMU calibration, and robot URDF metadata.

3. 03

   Enrich every clip

   Experts add semantic labels, segment trajectories into sub-tasks, and validate cross-sensor sync. Depth scales from bounding boxes and success flags up to contact-force and language annotations.

4. 04

   Deliver and validate

   Episodes ship in the requested format: HDF5 groups observations and actions, MCAP preserves ROS message types and timestamps, RLDS loads into TensorFlow Datasets with no preprocessing. Each delivery includes a datasheet of protocols and known limitations.

Other alternatives worth a look

Scale AI runs managed end-to-end pipelines for autonomous vehicles and robotics and, through its Universal Robots work, does industrial teleoperation capture; expect enterprise pricing. Labelbox and Encord are strong annotation tools (3D point clouds, video tracking, active learning), but neither runs a capture network, so you still supply the raw sensor data. For open source, LeRobot ships loaders, training scripts, and pretrained policies, and RoboNet offers 15 million frames across 7 robots, though its 2019 vintage misses recent hardware like the Franka FR3 and UR20 and contact-rich tasks.

How to choose

Choose Blomega or Blolabel if you are fine-tuning language models with RLHF, want a mobile-first labeling app for text or image data, and have no physical-world requirement. Choose Truelabel if you are training manipulation, navigation, or teleoperation policies and need real episodes captured to spec with provenance and robotics-native formats. Choose Scale AI when you need managed SLAs and regulatory support (ISO 26262, EU AI Act) and can absorb enterprise pricing. Reach for Labelbox, Encord, or an open-source stack like LeRobot only when you already hold the raw data and just need tooling to annotate or train on it.

Related pages

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

External references and source context

1. RT-1: Robotics Transformer for Real-World Control at Scale

   RT-1 was trained on 130,000 robot demonstrations spanning 700+ tasks

   arXiv ↩
2. Project site

   DROID dataset contains 76,000 manipulation trajectories across 564 scenes and 84 tasks

   droid-dataset.github.io ↩

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