RoboBILT: Why physical AI needs its own evaluation framework

Last November, we introduced the Toloka Quality Loop, our closed-feedback system for maintaining data quality at scale across AI training pipelines. The principle behind it is straightforward: quality has to be engineered into every stage of a pipeline. Robotics pushes that idea further than anything we've worked on before.

The problem with "success rate"

Most benchmarks in robotics report success rate, with the task either completing or not. The metric has some use, but it falls apart under the spotlight.

A robot arm that picks up an object while moving erratically, narrowly avoiding a collision, and taking four times longer than necessary has technically succeeded. Nobody would call that good policy behavior, however. And as physical AI moves from research into the field, the data decisions being made right now will shape what is trained at scale. The quality standards established today have a way of becoming the defaults that stick.

The issue is that physical AI produces data that is fundamentally different in character. It's multi-modal and time-sensitive in ways text annotation isn't. Think camera feeds, joint angles, force sensors, and action signals all synchronized to millisecond precision. 

A 20ms timing offset between camera and proprioception streams might be tolerable for some manipulation tasks. But for contact-rich work it can corrupt your training signal entirely. 

Sensor miscalibration often goes unnoticed unless it’s actively monitored. Small, gradual changes in sensing accuracy can build up over time and distort trajectories in ways that don’t prevent task completion but fundamentally weaken the quality of the data.

There's also a physical safety dimension with no equivalent elsewhere. ISO 10218 and ISO/TS 15066 set hard limits on contact forces during human-robot interaction. If your data collection pipeline isn’t enforcing those standards at the site level, you have a compliance problem before data quality even enters the discussion.

What existing frameworks miss

Most teams handling robotics data are either borrowing metrics from NLP and computer vision that don't map cleanly to physical systems, or running ad hoc internal checklists that don't generalize across projects. There's no widely adopted, unified quality standard purpose-built for robotics data and policy evaluation.

This is the gap we built RoboBILT to fill.

Introducing RoboBILT

RoboBILT is an evaluation framework for physical AI, extending the BILT quality principles that underpin our Quality Loop into the specific demands of robotics data and policy evaluation.

Where standard benchmarks assess outcomes, RoboBILT assesses quality hierarchically: at the frame level, the step level, the episode level, and across the full dataset. Each level catches problems the others miss. A corrupted image frame is a different class of problem from an implausible joint velocity spike, which itself is different again from a trajectory that succeeds but reflects poor execution quality, which, again, is different from a benchmark suite skewed heavily toward easy tasks.

The framework covers the characteristics that determine whether robotics data is trustworthy enough to train on: whether multi-modal streams are properly synchronized, whether trajectories are physically plausible, whether execution quality holds up under varied conditions. 

The sim-to-real question receives particular attention. Robotics data is inherently noisy, with sensor drift, timing misalignment, calibration decay, and environmental variation all introducing distortion that compounds across a pipeline. 

RoboBILT is designed to surface that noise systematically, so teams can distinguish between data that's genuinely training-ready and data that's silently degrading their models. If your simulation evaluations don't hold up against real-world performance, RoboBILT helps you find out where the signal broke down.

The goal is a shared, defensible vocabulary for what quality means in this domain. One specific enough to move beyond abstract scoring and toward actionable diagnostics.

Five levels of validation

RoboBILT evaluates quality hierarchically across five levels, each catching problems the others miss:

Frame level

Validates whether individual sensor readings are physically valid and present. A corrupted depth image or an out-of-range joint reading is a problem you want caught before it propagates.

Step level 

Checks whether transitions between consecutive frames are physically plausible and temporally coherent. Impossible acceleration spikes or force readings that violate safety standards point to collection protocol failures.

Episode level

Assesses whether a complete trajectory reflects quality execution, not just task completion. A task can succeed while producing data that weakens your training signal.

Dataset level

Evaluates whether the full collection has the diversity, coverage, and failure representation needed to produce robust policies rather than brittle ones.

Sensor rig level

Monitors whether calibration and synchronization parameters remain accurate and stable over time. Gradual sensor drift is one of the most common, and least visible, sources of data degradation in physical AI pipelines.

The data infrastructure hasn't kept up

Physical AI is moving fast, and the data infrastructure around it hasn't kept pace. RoboBILT gives teams working on robotics models a framework to assess, score, and systematically improve trajectory data and evaluation pipelines. It’s grounded in physical standards and current research, with metrics designed specifically for physical AI.