Training Data for ANYbotics
ANYbotics is building advanced robotic systems. Here is how real-world data accelerates their development from prototype to production deployment.
About ANYbotics
ANYbotics develops autonomous legged robots for industrial inspection and hazardous environment operations. Founded in 2016 as a spin-off from ETH Zurich, the company builds ANYmal, a quadrupedal robot that autonomously navigates industrial facilities to perform inspection tasks. ANYbotics has raised over $50 million and deploys robots across energy, mining, and chemical facilities.
ANYbotics at a Glance
Known Data Requirements
ANYbotics' deployment in industrial inspection requires training data from real industrial environments — oil refineries, chemical plants, power stations, mines — where autonomous robots must navigate complex terrain, detect equipment anomalies, and operate safely around human workers. The diversity of industrial environments and the specific visual patterns of equipment defects demand purpose-collected data.
Industrial facility terrain recordings
Source: ANYmal deployment documentation and terrain adaptation research
Visual and geometric recordings of industrial facility terrain: grated walkways, stairs, ramps, gravel, wet surfaces, cable trays. ANYmal must locomote reliably across these surfaces in all conditions.
Equipment inspection imagery
Source: ANYbotics inspection workflow documentation
Close-range visual recordings of industrial equipment — valves, gauges, pipes, electrical panels — in various states (normal, degraded, failed) for training anomaly detection models.
Industrial navigation recordings
Source: ANYmal autonomous navigation research, ETH Zurich
Sensor data from navigation through industrial facilities capturing the specific obstacle types, passage widths, and dynamic conditions (workers, vehicles, open hatches) that ANYmal encounters.
How Claru Data Addresses These Needs
| Lab Need | Claru Offering | Rationale |
|---|---|---|
| Industrial facility terrain recordings | Custom Industrial LiDAR + Visual Collection | Claru can deploy sensors in partner industrial facilities to capture terrain geometry and visual appearance data across the diverse surfaces ANYmal must traverse. |
| Equipment inspection imagery | Custom Inspection Data Collection | Claru can coordinate close-range visual capture of industrial equipment across multiple facilities, building the defect detection training set ANYbotics needs for their inspection workflow. |
| Industrial navigation recordings | Custom Multi-Sensor Industrial Collection | Purpose-collected sensor data from diverse industrial environments provides the navigation training data that covers the specific obstacle types and passage geometries of real facilities. |
Technical Data Analysis
ANYbotics sits at the intersection of legged locomotion and industrial inspection — two domains where real-world data is both essential and difficult to obtain. ANYmal must navigate terrain that would challenge most wheeled or tracked robots: metal grated walkways, steep industrial stairs, uneven gravel surfaces, and areas cluttered with pipes, cables, and equipment.
The locomotion challenge for industrial environments is distinct from the outdoor walking that most legged robot research addresses. Industrial terrain includes specific surface types — grated metal with specific pitch, industrial stairs with non-standard dimensions, oil-slicked surfaces, and transitions between indoor and outdoor sections. Simulation can model some of these, but the contact dynamics of robot feet on grated metal or wet concrete require real-world data to calibrate.
The inspection task adds a perception dimension that locomotion alone does not address. ANYmal must not only navigate to inspection points but capture high-quality sensor data of equipment and then detect anomalies — corrosion, leaks, abnormal gauge readings, thermal hotspots. Training anomaly detection models requires labeled examples of both normal and degraded equipment conditions from real industrial facilities.
Scaling inspection to new facilities is the key growth challenge. Each industrial site has unique equipment configurations, terrain characteristics, and inspection requirements. ANYbotics must either collect site-specific training data for each new deployment or build models general enough to transfer across facilities. Diverse industrial environment data is the path to transferable models.
Key Research & References
- [1]Hutter et al.. “ANYmal — A Highly Mobile and Dynamic Quadrupedal Robot.” IROS 2016, 2016. Link
- [2]Lee et al.. “Learning Quadrupedal Locomotion over Challenging Terrain.” Science Robotics 2020, 2020. Link
- [3]Miki et al.. “Learning Robust Perceptive Locomotion for Quadrupedal Robots in the Wild.” Science Robotics 2022, 2022. Link
Frequently Asked Questions
ANYbotics' deployment in industrial inspection requires training data from real industrial environments — oil refineries, chemical plants, power stations, mines — where autonomous robots must navigate complex terrain, detect equipment anomalies, and operate safely around human workers. The diversity of industrial environments and the specific visual patterns of equipment defects demand purpose-collected data.
Simulation cannot faithfully model the contact dynamics, material properties, and environmental conditions that ANYbotics's robots encounter in deployment. Real-world data provides the distributional coverage that fills simulation gaps — authentic surfaces, lighting conditions, and object interactions from actual deployment environments.
Yes. Claru operates a global network of 10,000+ data collectors across 100+ cities who can capture teleoperated demonstrations, egocentric video, and sensor data in target environments using standardized recording protocols.
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