Evolving Robots for Planetary Exploration (and Factories)

NASA’s Perseverance rover reportedly carries around 10 million lines of code—and that’s before you count the ground systems and simulation infrastructure needed to keep it safe. That number isn’t just trivia. It’s a clue: the real bottleneck in robotics isn’t “can we build a robot?” It’s can we build the right robot for an environment we can’t fully predict.

That’s why the ideas discussed in Robot Talk Episode 120—Claire’s conversation with Emma Hart (Edinburgh Napier University) about evolutionary computation—land so well in the broader AI in Robotics & Automation series. Planetary exploration is the extreme case: delayed communication, unknown terrain, harsh physics, and no technician on-site. But the same problem shows up on Earth every day in warehouses, factories, mines, farms, and infrastructure inspection.

Here’s the stance I’ll take: most robotics programs waste time optimizing control policies for a body they picked too early. If you let AI “co-design” the robot’s body and its brain together—under realistic constraints—you get systems that are more robust, cheaper to deploy, and easier to adapt when the environment changes.

Why “evolving” robots is the right mental model

Answer first: Evolutionary algorithms are a practical way to search huge design spaces—robot shapes, materials, sensor layouts, and controllers—when you can’t write down a neat objective function or rely on perfect data.

Emma Hart’s work sits in evolutionary computation: algorithms inspired by biological evolution that iterate through variation → evaluation → selection. In robotics, that usually means:

• Proposing many candidate robot designs or control parameters
• Testing them in simulation (or carefully in hardware)
• Keeping the best performers and recombining/mutating them to produce the next generation

Evolution beats hand-tuning when requirements are fuzzy

If you’ve ever tried to deploy an autonomous mobile robot (AMR) in a messy facility, you’ve seen “requirements drift” in real time:

• Aisles get narrower after a layout change
• Lighting changes break perception
• Floor conditions vary by season (winter grit is real)
• Payloads shift from boxes to totes to irregular items

The traditional approach is to freeze the hardware and then endlessly tune software. The evolutionary approach flips this: treat the robot as a system, and optimize what matters—stability, energy use, traction, sensing reliability—as a whole.

Planetary exploration is the stress test for autonomy

Space robotics makes the challenge obvious:

• Latency: you can’t joystick a rover over every rock
• Uncertainty: you don’t get a “nice dataset” of the exact terrain
• Repair constraints: when something fails, you don’t swap parts

So the AI needs to produce designs and behaviors that survive surprises. That’s precisely the kind of setting where “evolving” solutions is a natural fit.

How evolutionary algorithms co-design robot bodies and brains

Answer first: The real power comes from co-evolution—optimizing morphology (body) and control (policy) together, because each shapes what the other can achieve.

When people say “AI for robotics,” they often mean reinforcement learning (RL) for control. RL is great—until it isn’t. RL can learn impressive policies, but it’s typically trained on a fixed platform. If the platform is slightly wrong for the job, the policy ends up compensating in brittle ways.

Evolutionary robotics takes a different route: it can search across combinations like:

• Wheel/leg configurations and suspension geometry
• Foot shapes and contact surfaces for traction
• Sensor placement (cameras vs depth vs tactile) and protective housings
• Control architectures (gaits, state machines, neural controllers)

A concrete example: traction vs efficiency trade-offs

On soft sand (planetary regolith or a dusty warehouse floor), you can “win” on traction by pushing harder. But that often costs energy, creates slip, and increases wear.

With evolutionary optimization, you can score candidates against multiple objectives:

• Distance traveled without getting stuck
• Energy per meter
• Stability margin (tip-over risk)
• Thermal limits (motors overheating)

Multi-objective evolutionary algorithms can produce a frontier of options instead of one brittle “best” solution. That matters in automation, because the “best” design depends on your actual constraints: battery size, maintenance intervals, safety rules, and cost.

The hidden win: you discover non-obvious designs

Engineers are smart, but human intuition is biased toward familiar shapes. Evolutionary search is indifferent to tradition. It will happily propose:

• Asymmetric solutions that handle one “bad” direction better (useful in constrained aisles)
• Counterintuitive sensor layouts that reduce occlusions
• Control policies that exploit a compliant structure instead of fighting it

This is why evolutionary approaches keep showing up in domains where failure is expensive and the environment is hard to model.

From Mars to manufacturing: the parallels are closer than you think

Answer first: Planetary robotics and industrial automation share the same core constraint: you’re deploying robots into environments that don’t behave like your simulation.

Swap “Mars” for “distribution center” and you still get:

• Uncertain obstacles (people, pallets, spills)
• Degraded sensing (dust, glare, fogging)
• Surface variation (ramps, dock plates, cracked concrete)
• Communication gaps (dead zones, interference)

Logistics: evolving policies for the messy middle

The warehouse dream is clean, gridded, perfectly labeled. The warehouse reality is late trucks, mixed SKUs, and temporary staging areas. Evolutionary methods can help you build robots that stay functional when:

• The cost map is wrong
• The floor friction changes
• The payload inertia varies more than expected

Even if you don’t evolve the entire chassis, evolving parameters—controller gains, navigation heuristics, recovery behaviors—can make AMRs more resilient.

Manufacturing: rapid retooling needs adaptable robots

In high-mix/low-volume production, the robot that’s perfect for Product A is often mediocre for Product B. Evolutionary computation is well-suited for:

• Gripper design optimization for irregular parts
• Fixture and end-effector co-design for assembly
• Motion planning parameter tuning under safety constraints

I’ve found that the biggest payoff is not shaving 2% off cycle time. It’s reducing the weeks of engineering effort spent doing “one-off” tuning every time a line changes.

Field robotics: the “other planet” is often a mine

Mining, offshore inspection, forestry, and agriculture bring back the same harsh realities as space: harsh conditions, limited access, and high penalties for failure.

Evolutionary approaches can support design-to-environment thinking:

• Optimize locomotion for mud, rocks, or snow
• Prioritize redundancy when repair is difficult
• Trade speed for survivability when downtime is costly

The hard part: simulation, reality gaps, and safety constraints

Answer first: Evolutionary robotics only works in practice when you treat simulation as a tool, not a truth—and when you bake safety and manufacturability into the search.

There’s a reason you don’t see companies “evolving robots” overnight. The common objections are valid:

• “Simulation isn’t real.”
• “It’ll find weird designs we can’t manufacture.”
• “We can’t risk unsafe behavior in the real world.”

Here’s how serious teams handle those issues.

Use constraints, not hope

If you don’t constrain the search, the algorithm will propose fragile, exotic solutions. Add constraints early:

• Material limits (strength, weight, temperature)
• Actuator limits (torque, speed, duty cycle)
• Safety envelopes (max forces near humans)
• Manufacturability constraints (minimum feature size, standard components)

A good evolutionary setup is less like “let it run wild” and more like automated engineering trade-space exploration.

Close the sim-to-real gap with deliberate noise

One practical technique is domain randomization: vary friction, mass, sensor noise, delays, and terrain parameters during evaluation. The goal isn’t a perfect simulator. It’s a policy and design that’s robust across plausible realities.

If you’re applying this to automation, you can randomize:

• Wheel friction coefficients (clean vs dusty)
• Payload mass distribution
• Lighting and reflectivity for vision
• Wireless latency/dropouts

Robots that only work in the “nominal” scenario aren’t production-ready.

Safety: evolve recovery behaviors, not just performance

In industrial settings, a robot that fails safely is more valuable than one that occasionally wins big and occasionally crashes.

When evolving controllers, explicitly score:

• Recovery success rate (unstuck, reroute, re-localize)
• Near-miss events
• Maximum contact forces
• Time-to-safe-stop

This reframes autonomy as reliability engineering—which is exactly how buyers think.

A practical playbook: how to apply evolutionary AI in your robotics program

Answer first: Start by evolving what you can measure cheaply (parameters and behaviors), then expand toward co-design as your simulation and test infrastructure matures.

If you’re leading an AI robotics initiative in 2026 planning, here’s a pragmatic sequence that drives results without boiling the ocean.

1) Pick one “pain metric” and optimize it

Good starter metrics include:

• Docking failure rate
• Average recovery time from localization loss
• Energy per mission
• Slip events per kilometer

Tie it to cost. If a failure triggers a human intervention that costs $15, you now have a number the business will care about.

2) Evolve parameters before you evolve hardware

Most teams can start with:

• Navigation stack parameters
• PID gains and control limits
• Heuristic weights (risk vs speed)
• Behavior tree thresholds

This gets you the process—automated evaluation, reproducible testing, safe rollback—before you touch physical redesign.

3) Build a “digital test range,” not a perfect digital twin

A perfect digital twin is a long project. A useful test range is faster:

• 10–20 scenario families (tight aisle, reflective floor, ramp, clutter)
• Randomized noise and friction
• Automated scoring and logging

You’re training robustness, not vanity metrics.

4) When you’re ready, co-design the end-effector first

Co-designing an entire mobile base is heavy. Co-designing a gripper or tool is often a quicker win:

• It’s cheaper to prototype
• It’s easier to validate safely
• It has a direct impact on throughput and quality

5) Treat “weird” solutions as hypotheses

If the algorithm suggests an odd geometry or behavior, don’t reject it reflexively. Ask:

• What constraint did we miss?
• What edge case is it exploiting?
• Can we capture the underlying principle in a manufacturable form?

Some of the best designs start out looking wrong.

Snippet-worthy truth: If you don’t like the solution evolution found, your constraints are probably incomplete.

Where this is heading in 2026: autonomy that adapts, not autonomy that assumes

The next phase of AI in robotics & automation is less about flashy demos and more about systems that keep working after the second, third, and fiftieth unexpected change. Planetary exploration forces that mindset. So do real factories.

Emma Hart’s work is a reminder that AI doesn’t have to mean “train a bigger neural net.” Sometimes the smarter move is to search: explore design options, test them hard, and keep what survives.

If you’re building robots for logistics or manufacturing, the opportunity is straightforward: use evolutionary computation to reduce deployment friction, improve reliability in edge cases, and shorten the iteration loop between design and reality.

The question I’m left with—especially as more companies push autonomy into less structured spaces—is this: are you optimizing a robot you already have, or are you optimizing for the robot you actually need?