A person crouches behind a spruce trunk at dusk. Far behind them, a quadruped robot searches the fog with a single work light.

MS-001 · First Contact · In development

You are no longer the apex species.

Enter the proving ground. Outsmart the machines. Discover what humans still do better.

What is MechaSafari?

A few humans. A very large forest. Autonomous machines that are looking for them.

The rest is up to you.

This is not a simulation.

  1. 01

    Enter

    You and a small team enter an instrumented wilderness environment.

  2. 02

    Mission

    Reach extraction. Recover an object. Protect a location. Evade detection. Complete the objective.

  3. 03

    Contact

    Autonomous machines enter the field.

  4. 04

    Adapt

    They search.
    You improvise.

  5. 05

    Survive

    Everything is recorded. What happens next becomes part of MechaSafari.

At last light above a lake, a person lies hidden in moss while a quadruped robot steps through the fog a few metres away.

Scenario MS-001

First Contact

Reach extraction.
Don't let them find you.

Status: In development

Humans
4
Objective
1
Machines
Multiple
Duration
90min

Controlled by design

Adversary classes

The machines.

Five classes of autonomous system. Real categories from real robotics, no brands and no science fiction.

Classes, not brands. MechaSafari has no affiliation with any robot manufacturer.

The safari is only the surface.

Every MechaSafari encounter is an experiment.

Humans behave unpredictably. That unpredictability is valuable.

Our proving ground is designed to expose embodied AI to adversarial real-world conditions, and to capture systematically what happens.

From encounter to evidence
  1. Humans

    Motivated, creative, unpredictable.

  2. Encounters

    Instrumented runs in messy terrain.

  3. Discoveries

    Behaviour nobody planned for.

  4. Validation

    Reproduced, or it didn't happen.

  5. Benchmarks

    Comparable across platforms.

  6. Safer machines

    The point of all of it.

The human is not training data.

Human ingenuity creates intellectual value. We intend to treat it that way.

Participants will find things: machine weaknesses, strange behaviours, evasions, edge cases, strategies nobody scripted.

The easy path is to absorb all of it, silently, into somebody else's machine-learning corpus. We intend to do the opposite. Identify the discoveries that matter. Validate them. Catalog them. Credit the people who made them, and share the value they create.

  1. 01Identify
  2. 02Validate
  3. 03Catalog
  4. 04Attribute
  5. 05Share value

Prototype records Illustrative only. No encounters have run yet; these show the format, not findings.

MS-ADV-00017Specimen

Partial occlusion tracking failure

Discovered during
MS-001
Originated by
Participant, credited
Validated
3 platforms
Status
Reproducible
MS-ADV-00031Specimen

Group-split target prioritisation conflict

Discovered during
MS-001
Originated by
Team of four, credited
Validated
1 platform
Status
Under validation
MS-ADV-00046Specimen

Low-sun thermal contrast collapse

Discovered during
MS-001
Originated by
Participant, credited
Validated
Pending
Status
Submitted

Bring us something you think can't be broken.

Robotics teams spend enormous effort teaching machines to succeed. MechaSafari is being designed to discover, systematically, how they fail.

In a field tent, two technicians inspect a mud-caked quadruped robot with a leg panel removed.
After the run: every failure is a data point.

This is what we're building, not a list of things we've done. No certifications, datasets, customers or partnerships are claimed here. Early partners help write the test protocols.

The media engine

Every run tells a story.

Every encounter is filmed from both sides: the humans, the machines, the air, the heat and the data. The experience produces its own story.

Human camera: a participant's eyes under a wet beanie, pressed flat behind a mossy log.
Human POV · Cam 03
Machine camera: a participant climbs a mossy slope ahead of the robot.
Machine POV · Q-02
Thermal view: a crouching person glows white-hot behind a fallen log while a robot searches nearby.
Thermal · Mast 4
Drone view over a vast dark spruce forest and a lake at dusk.
Drone · Overwatch
Telemetry · Simulated
Map replay · Run 00

Concept feeds. Imagery is rendered and telemetry is simulated; no runs have happened yet.

Founding principles

  1. Humans should understand the machines entering their world.
  2. Capability deserves scrutiny.
  3. Failure is information.
  4. Human ingenuity has value.
  5. Autonomy requires accountability.
  6. The ability to stop a machine matters as much as the ability to start one.

We are fascinated by robotics. We are not Luddites. We think extraordinary machines deserve extraordinary testing.

Measure capability. Expose failure. Validate safety. Preserve human agency.

Read the principles

Interest register

Join the first hunt.

No dates, no prices, no countdown. Tell us who you are and we'll tell you when this becomes real.

I'm interested as

No spam, no list-selling. We'll write when there's something real to say.

Origin: 08:17

MechaSafari began as an objectively ridiculous text conversation about hunting robots in the woods.

At 08:24, mechasafari.com was registered for $11.48.

The joke lasted approximately ten minutes.

Then we realised the testing environment might be more valuable than the robots.