97.5%
Clean runs in a 1,000-run simulator test.
Autonomous AI Validation
Goodness Technology builds safety validation for autonomous AI systems.
Our first demo shows an adaptive robot controller that plans, recovers, and explains safer behavior in simulation before real-world pilots.
96.5%
Fewer crash-runs than the basic robot in the best tested mode.
975/1,000
Runs finished without collision by the Goodness robot.
Main Idea
Before autonomous AI acts, Goodness tests it.
The robot demo is the first proof point for a broader validation layer: testing, scoring, and explaining autonomous behavior before it reaches real environments.
Plan before moving
The robot samples possible paths, scores risk, and chooses safer motion in real time.
Adapt to the scene
Open space, traffic crossings, and narrow corridors produce different driving behavior.
Remember recent danger
The system treats problem areas with caution instead of repeating avoidable mistakes.
Validate before pilots
Benchmarks and telemetry make behavior reviewable before physical deployment.
Why This Matters
The next AI safety question is action.
As AI moves from chat into agents, robots, vehicles, workflows, and physical systems, the question is no longer just “can the AI answer?”
The question is: should this AI be allowed to act?
Goodness is building the validation layer that tests, scores, and explains autonomous behavior before it reaches the real world.
Benchmark Proof
Same test. Very different result.
In 1,000 randomized simulator trials, the Goodness robot finished clean far more often than the basic controller. Detailed controller internals remain confidential.
| What happened | Basic robot | Goodness robot | Plain-English takeaway |
|---|---|---|---|
| Clean runs | 287 / 1,000 | 975 / 1,000 | Ours finishes clean far more often. |
| Runs with a collision | 713 | 25 | Same test. About 28x fewer bad runs. |
| Total collisions | 1,720 | 28 | The basic robot keeps making the same mistake. |
| Safety interruptions | 561,867 | 19,058 | Ours needs fewer emergency corrections. |
| Can explain itself | No | Yes | Live telemetry shows risk, mode, skill, and recovery state. |
How it was tested
1,000 simulator runs across obstacle gauntlet, crossing traffic, and tight corridor scenarios.
Why it matters
Best tested mode cut collision-runs by 96.5% versus the basic robot.
The honest line
This is strong simulator proof. It is not yet a real-world safety certification.
Technology
What changes inside the robot behavior.
The system combines lookahead planning, bounded adaptation, recent memory, and readable state reporting so the robot is not a black box during a demo or review.
It looks ahead
It checks multiple possible moves before choosing where to steer.
It changes style
A tight corridor, crossing obstacle, and open path do not get the same behavior.
It remembers trouble
If a spot caused problems before, the robot treats that area with more caution.
It stays inside guardrails
The learning layer can tune within limits, but it cannot rewrite the controller on a whim.
Technical Snapshot
Simulation stack details for technical review.
These are the current demo and validation settings from the repository. They describe the simulated autonomy stack, not final production hardware.
Planning and Control
- Planning fan
- 31 candidate rays
- Planning cone
- 120 degrees
- Ray range
- 340 simulation units
- Base steering rate
- 7.5 rad/s
Speed and Safety
- Cruise speed
- 120 units/s baseline profile
- Caution speed
- 84 units/s
- Clarify speed
- 36 units/s
- Emergency distance
- 16 units, 0.38s confirm window
Memory and Learning
- Spatial memory
- 240 max entries
- Memory half-life
- 45 seconds
- Experience memory
- 180 reward/risk entries
- Learning boundary
- bounded skill/profile adjustments
Validation Harness
- Benchmark size
- 1,000 randomized runs
- Trial window
- 45 seconds per run
- Simulation step
- 60 Hz
- Scenario set
- gauntlet, crossing, corridor
The robot body in the simulator uses a 13-unit collision radius. Skill profiles include baseline, projectile dodge, corridor precision, and flow runner modes.
Live Demonstration
Watch it run. Then try to break it.
The embedded simulator shows the robot moving through obstacles in real time. Open the full demo for controls, telemetry, scenario changes, and side-by-side behavior.
Phone viewing is supported for quick preview, but the full simulator is best on desktop or tablet.
Start with the baseline
Watch how often a simple controller gets trapped by traffic, corners, or repeated hazards.
Switch scenarios
Use gauntlet, crossing, and corridor cases to see how the robot changes behavior.
Read the telemetry
Look for mode, risk, recovery state, and skill behavior while the robot moves.
Embedded demo could not initialize in this browser context. Open the full demo directly.
Current Stage
Working simulation now. Physical pilots next.
We are being clear about what is proven today, what comes next, and who we want to meet.
Current stage
Working simulation demo and benchmark.
Next stage
Controlled physical robot pilot.
Not claiming yet
Production robot safety certification.
Looking for
Pilot partners, advisors, and early investor conversations.
Who We Want To Meet
People building, funding, or validating autonomous systems.
Robotics founders
Mobile robots, warehouse robots, service robots, and other embodied AI systems.
AI safety researchers
Researchers interested in autonomous system validation, scoring, and explainability.
Investors
Investors focused on physical AI, robotics, infrastructure, and safety.
Enterprise teams
Innovation teams testing AI agents, autonomous workflows, or operational AI risk.
Technical advisors
Advisors in robotics, simulation, controls, validation, and embodied AI.
Market Context
Autonomous robots are becoming a real operating budget.
The opportunity is not just selling a robot. It is making mobile robots safer, easier to trust, and easier to validate before they enter real operations.
AMR market by 2033
Grand View Research estimates autonomous mobile robots reach $14.04B by 2033.
Mobile robotics by 2030
Grand View Research projects the broader mobile robotics market at $73.68B by 2030.
U.S. AMR market by 2033
The U.S. AMR market is projected to grow from $650M in 2025 to $1.87B by 2033.
| Market group | Examples | What it shows | Goodness angle |
|---|---|---|---|
| Warehouse AMR fleets | Locus Robotics, MiR, OTTO Motors, Vecna, GreyOrange | Customers pay for mobile robots when reliability and workflow ROI are clear. | Autonomy behavior, validation, and explainability layer. |
| Industrial automation incumbents | Rockwell Automation, Teradyne Robotics | Large automation companies are buying or building AMR capability. | Potential partner, licensing, or acquisition path if results mature. |
| Physical AI platforms | Skild AI and other robot intelligence companies | Investors are funding the robot “brain,” not only the robot body. | Narrower, evidence-led control intelligence for dynamic navigation. |
Market figures are third-party estimates and are not a valuation of Goodness Technology Company. They show the commercial direction of the category.
Contact
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