Why DataX Power Is the Best Vietnam Data Collection Partner for Robotics AI

If you are shortlisting a Vietnam data collection partner for a robotics or embodied AI program, you will find plenty of vendors who can point a camera at a task. Fewer can deliver footage your policy can learn from. That means real people recorded in real environments, synchronized views, a consent record for every session, and a dataset structure your training code reads without a week of conversion work.

We started DataX Power in 2023. The company is headquartered in Hanoi with a presence in Melbourne, and works across three pillars: custom AI model development, data services (collection, annotation, validation) and infrastructure operations. Our annotation arm operates as DataXanno. For the past two years we have run hands-on AI and robotics data collection and processing programs for teams working on manipulation policies and humanoids, as well as autonomous systems and smart-glasses products.

We think we are the strongest choice for robotics teams collecting in Vietnam and Southeast Asia, for the five reasons set out below, each with the operating detail behind it. We also say where a different kind of supplier may suit you better. This is our own page, so read it the way you would read any vendor's case, then test it with a pilot.

Most robotics data problems trace back to a gap between where data was recorded and where the robot has to work. A policy trained on a tidy lab bench can stall in a cluttered Bangkok kitchen or a narrow warehouse aisle in Johor. The DROID project made the same point at research scale: its authors collected 76k manipulation trajectories across 564 scenes and report that training on that diversity produced policies with higher performance, greater robustness and better generalization.

Our operating model is built to close that gap. What we collect is real-world footage, meaning real environments and real participants, with one team managing every step between your brief and the delivered dataset.

Two collection methods sit under that model. Onsite and studio collection gives tight control over lighting and rig calibration, so sessions are repeatable. Crowdsourced collection buys breadth across many homes, shops and streets, and shipped kits plus protocol checks keep it within spec. The two can run in one program, for example a studio anchor set for controlled comparisons plus field sessions for scene diversity.

What separates this from an open crowd platform is accountability. We choose the participants and write the protocol. We set up the hardware, and footage that fails the pass criteria is rejected before it reaches you. If something goes wrong anywhere in the chain, you have one team to hold responsible instead of a marketplace of individual contributors. Every program covers the same six managed steps:

We have spent the past two years collecting and processing data for AI and robotics programs, including robotics data collection in Vietnam and the wider region. Those two years were focused entirely on this work, and they cover the period in which robot learning shifted toward large, real-world demonstration datasets: the Open X-Embodiment collaboration pooled data from 22 robot types across 21 institutions into standardized formats, and projects like DROID showed what distributed real-world collection can do.

What we have from that time is a working knowledge of how robotics collection fails, and it fails differently from general video work. A demonstration that looks complete but ends before the object is released. A head-mounted clip where the hands leave the frame during the critical grasp. Two camera streams that drift a few frames apart over a long session. A task list that covers the easy variations fifty times and the hard ones twice. A thumbnail review catches none of these, and every one of them hurts a policy.

Over more than 100,000 collection hours executed, we have written those lessons into our capture protocols and QA checklists, and into how we train participants, instead of leaving them in individual heads. A new program starts from that baseline, so you are not paying for us to relearn the same mistakes.

Collecting data is only one part of what we do. Custom AI model development is one of our three business pillars. Our engineers build and train models for clients, and that changes how we collect.

Training models teaches you quickly which data properties matter. Label consistency beats label volume. Failure cases need coverage and task distributions need balance. Timestamps have to be accurate, and metadata should let you slice results by scene, participant or lighting. So when our collection leads write a capture protocol, they are thinking about the training run as much as the recording day.

At scoping we ask questions that are easy to skip if you only ever deliver raw footage. What action space and observation keys does your policy use? What control frequency? Which camera views are inputs at inference time? How do you split training and evaluation data? Which failure modes are you seeing today? The answers go into the protocol and the rig, and into the QA pass criteria. Without them, a pilot tends to come back as a footage sample instead of something a training team can use.

Raw footage is not a dataset. The value gets added between capture and training, which is where our data services and AI development teams work together.

The step that is easiest to leave out is the loop back from training to collection. A supplier that stops at delivery cannot tell you that your policy struggles with transparent objects or low light. When the same team trains models, it sees those failures and can change the next batch: the lighting, the scene mix, the balance of tasks.

Format matters for the same reason. RLDS was designed as a standard, lossless format for recording and sharing sequential decision-making data, and LeRobot stores synchronized video alongside Parquet files for state and action data. Delivering in structures like these, or in your internal schema, means your engineers spend their time training rather than writing converters. Each program runs through the same path:

Two capture setups come up again and again in robot learning programs, and we have run both for robotics training.

Stereo camera egocentric collection uses head-mounted rigs with calibrated camera pairs, so the footage carries depth cues about hand-object distance rather than a flat first-person view. That matters for manipulation and hand-object interaction learning, where a monocular clip can hide how far the hand really is from the object. The operational work is keeping calibration stable while the rig moves with the wearer's head. On top of that, the setup has to stay consistent across participants and sessions, with both streams frame-aligned. Our egocentric capture runs on head-mounted rigs, Meta Aria glasses and smart glasses at up to 4K/60fps.

Multi-camera video collection places several synchronized cameras around the task. The hard work is in timestamp alignment and in keeping camera placement consistent across sessions, and QA has to check every view instead of only the primary one. We cover four capture modes, which can be combined in a single program:

Robotics buyers usually compare four kinds of supplier. Each has a legitimate place, and the right one depends on what you need at the end. Here is how they differ for robotics training data, as we see it.

The numbers first. A pilot specification is usually ready within 5 business days, and onboarding takes about two weeks. A pilot can start at 100 hours, and the same contract scales to 50,000-hour production programs without a new RFP. To date we have executed more than 100,000 collection hours.

Scale on its own proves little. A 50,000-hour program only pays off if the first 100 hours prove the protocol, so we treat the pilot as a joint test rather than a sales formality. By the end of it, both sides should know whether the spec holds up, whether the rig works, whether the participants are the right ones and whether the QA criteria catch what they should. Our collection work covers five industry areas:

We would rather lose a poor-fit program than run it badly. If your need is labeling a large archive you already own, you do not need a collection program at all; an annotation provider, including our DataXanno team, is the faster route. For participants in a country outside Vietnam, Thailand, Singapore and Malaysia, raise it at the first call and we will tell you plainly what we can cover rather than improvise a network. And if what you want is a public benchmark rather than proprietary data, open datasets such as DROID may be the quicker start, with custom collection added later to cover your deployment environments.

Robotics and ML leads evaluating DataX Power as a Vietnam data collection partner usually ask these first.