What is defense robotics data collection?
Defense robotics data collection is the capture of sensor data and operator demonstrations that train and test autonomous systems for military logistics, explosive ordnance disposal, maintenance inspection, and installation security. It focuses on degraded conditions (no GPS, lost communications, night, smoke, rough terrain) and is run under data handling controls set by the program before the first sensor turns on.
Autonomy is now a procurement priority. The Defense Innovation Unit's Replicator initiative set out to field all-domain attritable autonomous systems at a scale of multiple thousands by August 2025, with a second iteration focused on countering small drones around critical installations.
Data handling is tightening in parallel. The DFARS rule putting CMMC into contracts took effect November 10, 2025, with third-party Level 2 assessments phasing in from November 2026. For robotics teams, that means how training data is stored and handled is now part of winning the contract.
What we support, and what we don't
Gamasome works on non-weapons autonomy: systems that move supplies, handle hazards, inspect equipment, and help protect installations. We don't collect data for weapons targeting or engagement. Stating that up front saves both sides time.
| Application | What the system must learn | Data we collect | Services |
|---|---|---|---|
| Logistics and resupply ground vehicles | Off-road driving without GPS, in dust, mud, and at night | LiDAR, camera, thermal, and IMU runs across terrain and conditions | LiDAR, edge case |
| EOD and hazardous-material robots | Fine manipulation under teleoperation, grip on unknown objects | Teleoperated demonstrations with inert training items, force and wrist cameras | Teleop, multimodal |
| Vehicle and aircraft maintenance inspection | Corrosion, damage, and fastener checks | 3D scans and drone or crawler imagery of airframes and vehicles | Point cloud, aerial |
| Installation perimeter sensing | People, vehicles, and small drones vs birds and clutter | Camera, thermal, and LiDAR capture at perimeters by time and weather | Multimodal, edge case |
| Degraded-mode evaluation | Performance when GPS, comms, or visibility is lost | Held-out scenario sets for test events | Edge case |
The Controlled Data Chain: handling decided before capture
Gamasome framework
In defense work, a technically perfect dataset can still be unusable if it was handled wrong. The Controlled Data Chain sets how data will be marked, stored, and accessed before collection starts, then carries that record through delivery.
Handling rules decided after capture are handling rules that were already broken.
Classification up front
The program's data category (public, controlled unclassified, or export-controlled) is agreed during scoping, and the capture plan is built around it.
People and access
Who can collect, label, and view the data is defined by the contract's requirements, not by who's available that week.
Storage and location
Where data lives, and whether it can leave a facility or country, is fixed in writing before the first session.
Chain of custody
Every file carries provenance: sensor, calibration, time, place, and every hand it passed through.
Degraded-mode coverage
GPS loss, comms loss, night, smoke, and dust are planned as coverage targets, because they're the conditions that decide mission success.
What a ground logistics data program looks like
Composite scenario: autonomous resupply vehicle (details generalized)
- Situation
- A team building an autonomous resupply vehicle has solid performance on graded test tracks with good GPS.
- Problem
- Field trials happened in wooded terrain with patchy GPS, and at dusk. The vehicle slowed to a crawl whenever its position estimate degraded.
- Solution
- LiDAR, thermal, camera, and IMU runs on representative terrain with GPS deliberately degraded, across dusk and night, with every run tagged by positioning quality and visibility.
- Outcome
- Training data for the conditions trials actually exposed, and a held-out route set for the next test event.
For the program manager, what matters is walking into the next test event with evidence the vehicle was trained for the terrain it'll face.
Common mistakes in defense robotics data
Deciding handling rules late
Retroactively marking or segregating data is slow and sometimes impossible. Decide first.
Test-range-only data
Graded ranges with strong GPS don't represent operational terrain. Plan degraded conditions on purpose.
Skipping operator expertise
EOD technicians and vehicle operators know the task. Their teleoperated demonstrations are some of the most valuable data you can get.
No held-out set for test events
Without data the model never saw, a test event measures memory, not capability.
For civil infrastructure and homeland programs, see our national security page. Test suites built from held-out scenarios run through our validation and testing service.
Defense robotics data FAQs
What is defense robotics data collection?
It's the capture of sensor data and operator demonstrations that train and test autonomous systems for military logistics, explosive ordnance disposal, maintenance inspection, and installation security, focused on degraded conditions such as GPS loss, night, and rough terrain.
Which defense applications does Gamasome support?
Non-weapons autonomy: logistics and resupply vehicles, EOD and hazardous-material robots, maintenance inspection, installation perimeter sensing, and evaluation data. We don't collect data for weapons targeting or engagement.
How do you handle controlled or sensitive data?
Data category, access, storage location, and chain of custody are agreed during scoping and built into the capture plan. Where a program requires specific certifications or clearances, we confirm up front what we can support.
Can you collect data in GPS-denied conditions?
Yes. We capture LiDAR, camera, thermal, and IMU data with reference positioning so autonomy can be trained and evaluated when satellite navigation is degraded or unavailable.
How do you capture EOD robot training data safely?
Teleoperated demonstrations use inert training items and follow range safety procedures, recording the robot's states, wrist cameras, and force data.
How does this relate to your national security work?
Defense covers military autonomy programs. Our national security page covers critical infrastructure and homeland programs such as substation, port, and facility protection.