← Back to research
Joint validation in progressPrototype / PoC applications open

TIME TO NEXT EXPERIMENT

RFR

Robot Flight Recorder

Spend less time debugging. Spend more time validating value.

RFR connects device communication inside a robot, embedded Linux, LTE, and cloud evidence on a shared timeline to shorten the time it takes physical AI teams to return to their next valuable real-world experiment.

Visit the RFR public website

RFR

TIME TO NEXT EXPERIMENT

Spend less time debugging. Spend more time validating value.

Connect
Detect
Reconstruct
Continue

CHALLENGE

The problem

Robot development teams often lose time to protocol discovery, log preparation, waiting for failures to recur, clock alignment, and handoffs between teams. When logs from the robot to the cloud use different formats and clocks, narrowing the scope of a failure becomes especially difficult and real-world value validation stops.

APPROACH

Current approach

RFR normalizes evidence from devices, ROS 2, Linux, modems, LTE, transport, cloud systems, and recovery processes into common events, aligns their clocks, and reconstructs a Failure Timeline. It separates the first observed deviation, evidence, uncertainty, and missing evidence to guide the next capture or check.

PUBLIC VALIDATION FLOW

How the current flow works

  1. 01

    Connect

    Understand the communication path and connect the equipment and required observation points.

  2. 02

    Detect

    Detect anomalies and preserve evidence from before and after a failure.

  3. 03

    Reconstruct

    Align different formats and clocks, then narrow the failure scope using evidence.

  4. 04

    Continue

    Use the findings to return to the next valuable real-world experiment.

CURRENT STAGE

Current research stage

The current v0 starts with one resolved mobile connectivity failure reconstructed from three to five available log sources. Connectivity Failure Reconstruction Sprints and connectivity or wireless PoCs for existing equipment are being validated jointly with partners.

Raw logs remain the primary evidence, and AI is limited to a read-only assistance layer. Areas that cannot be observed, such as internal mobile network operator data, are identified as Missing Evidence rather than filled with speculation.

About this information

This page summarizes information published on the RFR public website as an invilab research and development project. Public information reviewed: August 1, 2026.

The project is under research and development. Publication does not guarantee future commercial availability or implementation of every described capability.