Embedded Physical AI learns how bias, vibration, temperature, noise and dynamics affect a measurement while those effects are still observable—then publishes a cleaner, confidence-aware physical state.
Most robots receive measurements first and recover meaning later. Olive moves part of that understanding into the sensing node—before context is lost and before high-rate raw data has to cross the machine.
Preserve classical estimation and physical constraints. Use learned models for the nonlinear, coupled and operating-point-dependent residuals that static calibration cannot fully describe.
The model learns relationships across time, channels and operating conditions—at the point where their physical cause is still observable.
Learn residual bias after classical calibration, including warm-up behavior, long-term drift and operating-point dependence. The target is not to hide physics, but to model the portion a static constant cannot.
Recognize machine-specific resonances, structural coupling and repetitive disturbances. Preserve real physical events while reducing vibration-induced artifacts in the estimated state.
Model nonlinear response, transient lag and temperature history. Embedded context makes it possible to learn how the device arrived at its current thermal state—not only the current reading.
Learn context-dependent noise structure instead of assuming constant variance. Publish the physical estimate together with confidence, covariance, anomaly and health information.
Olive combines synchronized acquisition, classical estimation and embedded learning. The learned model handles residual complexity; engineering constraints preserve structure, observability and a deterministic fallback path.
Raw channels, device state, temperature and hardware timestamps.
Align multi-rate streams and preserve source-time context.
Apply calibration, kinematics and physics-based state estimation.
Model nonlinear, coupled and context-dependent effects on ARM / NPU.
State, confidence, events and health through native ROS 2 / DDS.
Simulation provides controlled variation. The lab provides repeatable excitation and ground truth. Field data provides the combinations reality creates. All three are needed for robust embedded models.
Use NVIDIA Omniverse, Isaac Sim and other physics environments for synthetic sensor data, controlled scenarios, domain randomization and rare operating conditions.
Build synchronized datasets under controlled motion, vibration, temperature and loading—with repeatable excitation and reference-grade supervision.
Capture installation effects, disturbances, operational regimes and failure signatures in representative robots, machines and environments.
Olive co-designs embedded compute, real-time software, time-series AI and robot-native communication. That keeps the complete path—from acquisition to inference to publication—inside one observable system.
Embedded Physical AI reduces the burden on central compute and gives perception, planning and control a more reliable representation of the physical world.
Learn residual bias, vibration coupling and thermal behavior for more robust inertial state estimation.
Combine force, torque, motion and tool dynamics to identify interaction states and changing loads.
Extract machine state, changing spectral signatures and anomalies before raw data leaves the asset.
Learn cross-sensor relationships locally while preserving synchronized and interpretable outputs for the robot.
Turn sensing hardware into a local intelligence layer for robotics, automation and embodied AI.
The information, specifications, and materials provided on this website are published by Olive Robotics GmbH for general informational and commercial purposes only. While every effort is made to ensure accuracy and timeliness, all specifications, performance figures, availability details, and product roadmaps remain non-binding and subject to modification, withdrawal, or correction without prior notice. Nothing on this website shall be construed as constituting a warranted characteristic within the meaning of §§ 434 et seq. BGB, nor as a binding commercial offer within the meaning of § 145 BGB, unless expressly confirmed in writing by Olive Robotics GmbH.
Access to and use of this website is at the user’s sole risk. Olive Robotics GmbH disclaims, to the maximum extent permitted by applicable law, all liability for direct, indirect, incidental, or consequential damages arising from the use or misuse of this website, its content, or any linked external resources. Pursuant to §§ 7–10 TMG, Olive Robotics GmbH is not responsible for third-party content accessible via external hyperlinks and assumes no responsibility for the availability, accuracy, or legality of such content.
All trademarks, logos, product identifiers, and intellectual property referenced on this site, including but not limited to olive®, olixSense™, olixAI™, olixOS™, and related marks, are the exclusive property of Olive Robotics GmbH. Unauthorized reproduction, modification, or commercial use is strictly prohibited. References to third-party technologies or marks are for descriptive purposes only and imply no endorsement or affiliation.
All quotations, orders, and deliveries are governed exclusively by Olive Robotics GmbH’s General Terms and Conditions (AGB) as published on this website. For consumers, statutory rights under the German Civil Code (BGB) remain unaffected.
Olive Robotics GmbH
Embedded Physical AI for Robots.
