Stress-test what the robot sees
Cameras, RGBD and LiDAR models with noise and variation. Measure how perception stacks up under lighting, clutter and occlusion.
RoSi
RoSi is the physics-accurate simulation platform
for testing and validating Physical AI and classic robotics stacks.
Whether your robot runs a learned policy or a classic navigation stack,
the only way to trust it is digital validation - across every layout, light condition, obstacle and occurrence.
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SiL & HiL
On-board controllers
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Multi-fleet
Across robotics ecosystems
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Open-core
Extensible engine
Three levels
of testing & Validation
Start with a single module, scale up to a system of systems - in the same environment.
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Validate the whole stack end to end
Your classic ROS 2 stack or a learned policy: VLA, imitation learning, RL. In closed loop, across diverse scenarios.
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Validate behaviour in the system
Many AI-driven robots, people and legacy automation sharing one floor. Check coordination and safety before rollout.
RoSi core
capabilities
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Benchmark checkpoint equally
Turn evaluation from a demo on one robot into a regression suite that runs on every model update.
- Versioned test suites: scenes, seeds, tasks and success criteria
- Success rate with confidence intervals, not single anecdotal runs
- Side-by-side comparison of checkpoints and model families
- Recorded replays and metrics for every failed rollout
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Find the failure before your
customer doesSystematically vary the conditions that break learned behaviour and pinpoint exactly where the policy stops generalising.
- Unexpected obstacles in the robot’s path
- Lighting, reflections, sensor noise and partial occlusion
- Unseen objects, poses and task instructions
- Safety metrics: collisions, near misses and protective stops
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Turn findings and edge-cases
into training dataGenerate labelled datasets around the exact situations your policy gets wrong, for faster sim-to-real transfer.
- Automated dataset generation with varied lighting, sensors and environments
- Standard outputs for many AI/ML pipeline integrations
- Noise and variation models for RGB, RGBD and LiDAR
- Metrics and benchmarks to validate sim-to-real performance
Built on proven
robotics elements
The same platform our customers use today for ROS 2 development, multi-robot workflows and virtual commissioning.
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Multi-robot workflows
Optimise multi-robot and multi-system deployments, with parallel simulation and easy integrations.
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Large-scale sites
Simulate sites for real-world productivity and safety.
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Software & hardware in the loop
Test perception, localisation or planning modules on target hardware.
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ROS 2 native
Interoperability with ROS 2 and CI/CD tooling, sensor data connector between sim and real.
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Multi-vendor, multi-fleet
Integrate robots and fleet managers from different vendors across ecosystems.
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Open-core engine
Modular architecture designed for transparency, flexibility and long-term adaptability.
Seamless integration
with your current pipeline
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Policies & software
- Connect a learned policy
- Works with VLA, imitation learning and RL policies
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Robotics software integration
- ROS 2 topics and services out of the box
- Navigation and fleet management integration
- Run on target compute (HiL)
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CI/CD & cloud
- Integration into pipelines
- Parallel execution across many nodes
- REST API to trigger scenario execution
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Data & MLOps
- Standard dataset formats for training pipelines
- Per-run metrics exported for dashboards
- Replay logs and outputs for failure analysis
Test on your site,
not on a generic benchmark
A policy that scores well on a public benchmark can still fail in your aisles. SLINGSHOT turns scans of your facility into a SimReady twin.
- Site-specific geometry, materials and lighting
- Repeatable scenarios on the real operating layout