Humanoid Robot Tests That Actually Matter to Utilities

Most robotics demos still hide the hardest part: reliable work in messy, uncoached environments. That’s why Benjie Holson’s “Humanoid Olympics” idea is more useful than it looks. Not because utilities need robots making peanut butter sandwiches, but because the tasks expose the exact failure modes that keep robots out of substations, plants, and field trucks.

Energy and utilities leaders are already investing in AI for predictive maintenance, grid inspections, and field operations automation. The missing link is physical capability: getting AI-powered robots to touch the world safely, repeatably, and under time pressure. Holson’s events—doors, laundry, tools, fingertip manipulation, and wet manipulation—map surprisingly well to what a utility robot must do to become a real asset instead of a pilot project.

This post is part of our AI in Robotics & Automation series. The theme here is simple: the robots that win in real operations won’t be the ones with the flashiest videos—they’ll be the ones that can handle boring tasks on the worst day.

Why “everyday chores” are a serious robotics benchmark

Answer first: Household chores are a great proxy for field work because they demand dexterity, force control, and error recovery—the same capabilities utilities struggle to automate.

Utilities don’t have a “structured warehouse floor” problem. They have:

• Outdoor environments with mud, glare, rain, and wind
• Aging infrastructure with nonstandard hardware and undocumented variations
• Safety-critical work where the cost of a mistake is high
• Human-in-the-loop processes that are expensive and hard to scale

What makes Holson’s Humanoid Olympics interesting is that it’s not a vision-only benchmark or a “pick-and-place” contest. It’s a test of contact-rich manipulation. Doors fight you. Keys require precision under force. Wet cleaning punishes sloppy sealing and poor materials choices. Those are exactly the frictions utilities face when they try to operationalize robotics.

The current state-of-the-art (and its ceiling)

Holson summarizes what’s working today: learning from demonstration—often via teleoperation (puppeteering one robot to control another, or VR controllers). It’s productive because it can capture chaotic, high-dimensional motions (like tugging fabric into place).

But he also calls out limitations that matter directly for energy automation:

• Weak force feedback: If the operator can’t feel contact well, demonstrations don’t teach the robot the “right amount” of push, twist, or compliance.
• Limited finger control: Many robots still behave like sophisticated grippers, not hands.
• No real touch sensing: Human hands are sensor-dense; robot hands are not.
• Medium precision: Roughly centimeter-scale precision is common in real-world videos.

Here’s the stance I’ll take: Utilities shouldn’t wait for perfect humanoid hands. But they should track these limitations carefully, because they define what’s feasible in the next 12–36 months.

Event 1: Doors → access control, cabinets, and plant navigation

Answer first: If a robot can’t reliably operate doors, it can’t move through the utility world—substation gates, equipment cabinets, control-room access points, or even vehicle doors.

Holson ranks door difficulty from simple push doors to self-closing pull doors (the “boss fight”). That progression mirrors utility reality:

• Simple access: pushing open an interior door ≈ passing through a standard gate or lightweight enclosure
• Self-closing force: doors that push back ≈ spring-loaded cabinet doors and weather-sealed panels
• Pull doors with closure dynamics: the robot must coordinate limbs and body motion, not just hands

Utility translation: why door skill is not “nice to have”

A field robot that can’t handle doors forces expensive workarounds:

• Humans must escort it and stage access
• Facilities must be retrofitted (costly, slow, politically hard)
• Autonomy collapses the moment the robot meets a barrier

If you’re evaluating robotics vendors for grid inspection or plant rounds, ask a blunt question:

Can the robot open and pass through common doors and equipment cabinets autonomously, on video, in real time, with no edits?

That’s a better filter than most slide decks.

Event 2: Laundry → cable management, soft goods, and “non-rigid” maintenance work

Answer first: Laundry tasks are a stand-in for soft, deformable materials—exactly what shows up in utility maintenance as PPE, tarps, straps, hoses, and cable bundles.

Laundry sounds irrelevant until you map it to field operations:

• Inside-out T-shirt → turning and orienting flexible items (hose sleeves, protective covers)
• Sock inside-out → inserting a hand/arm into a deformable cavity (boot covers, conduit routing sleeves)
• Hang a dress shirt + button → aligning holes, applying pinch force, and doing precise insertion (think: connecting small fittings, aligning latches, securing tie-downs)

What’s valuable here is the concept of state explosion: fabric can be “correct” in many ways and “wrong” in many more ways. Utility environments are full of these ambiguous states.

Practical takeaway for utilities

If your automation roadmap includes robots handling anything flexible—cables, straps, hoses—plan for:

1. More sensing than you expect (vision plus contact cues)
2. More training data than you want (hundreds of short demonstrations per variation)
3. A constrained scope at first (one PPE type, one cable gauge range, one hose family)

Soft goods manipulation will not be a quick win. Budget and schedule accordingly.

Event 3: Tools → the real gateway to field operations automation

Answer first: Tool use is the dividing line between “robot can move” and “robot can work.” Utilities care about tool competence far more than humanlike walking.

Holson’s tool ladder goes from spraying cleaner to making sandwiches to using a key. Ignore the kitchen theme—the underlying capabilities are what matter:

• Trigger squeeze + aim: strength, stability, finger independence
• Knife grasp adjustment: in-hand regrasping and forceful contact against a surface
• Key selection and insertion: high-precision manipulation plus torque under constraint

Utility translation: the “tool triad” that predicts ROI

In my experience, early ROI for utility robotics clusters around three tool categories:

1. Inspection tools: thermal camera positioning, gas sniffers, ultrasonic sensors
2. Basic handling tools: brushes, wipes, simple clamp-on devices
3. Access tools: keys, latches, standardized quarter-turn fasteners

If a vendor can’t show reliable, repeatable tool grasp + tool force application + tool stow, their “autonomous maintenance” claims are premature.

Event 4: Fingertip manipulation → connectors, labels, and fasteners

Answer first: Fingertip skill predicts whether a robot can handle the small, annoying tasks that dominate maintenance time: connectors, tabs, zip ties, labels, and packaging.

Holson’s examples—rolling socks, opening a dog bag, peeling an orange—sound playful, but the mechanics are serious:

• separating thin films
• creating and maintaining a pinch grasp
• sliding material between fingertips
• applying force without tearing

Utility translation: where fingertip dexterity pays off

If you’ve ever watched a technician lose minutes to a stubborn connector or a protective film, you get it. Dexterity reduces:

• repetitive strain
• error rates in reassembly
• time spent on “micro-tasks” that don’t require human judgment

Here’s the hard truth: most industrial robots still avoid fingertip work by redesigning the environment. Utilities can do some redesign, but not at grid scale, not quickly.

Event 5: Wet manipulation → the non-negotiable test for real deployments

Answer first: Wet manipulation is the quickest way to separate lab-ready robots from field-ready robots, because utilities operate in water, dust, grease, and contamination.

Holson’s wet tasks (wiping counters, cleaning peanut butter off the hand, scrubbing a greasy pan) expose three operational requirements utilities care about:

1. Ingress protection and materials: seals, cable routing, corrosion resistance
2. Grip reliability under low friction: wet surfaces change contact dynamics
3. Cleanability and contamination control: a robot that can’t be cleaned becomes a safety risk

Utility translation: cleaning isn’t a side quest

Robots deployed in:

• power plants
• battery storage sites
• underground vaults
• coastal substations

…will face moisture, grime, and cleaning protocols. If a robot can’t tolerate routine washdown or controlled cleaning, it becomes downtime.

When you evaluate robotics for energy and utilities, ask for explicit answers on:

• what “wet” means in their spec (spray? splash? submersion?)
• cleaning procedures and allowable agents
• how they protect sensors and joints during contact with fluids

What this means for AI in energy & utilities (and what to do next)

Answer first: The fastest path to value is pairing AI with robotics in constrained, repeatable workflows—then expanding scope as manipulation reliability improves.

Holson’s rules for winning (autonomous, real-time video, no cuts, time limits) are a good mindset for utilities procurement: if it only works with perfect staging and a highlight reel, it’s not ready.

A practical 90-day pilot blueprint (that avoids the common traps)

If you’re exploring AI-powered robotics for field operations automation, here’s a grounded way to start:

1. Pick one location and one workflow
   • Example: daily inspection route in a substation control building
2. Constrain the manipulation surface area
   • Standardize a cabinet type, a handle type, a checklist order
3. Instrument the workflow
   • Log attempts, failures, time-to-complete, and intervention reasons
4. Require “no-edit” proof
   • Real-time runs reveal robustness faster than any metric report
5. Define success as reduced human exposure, not full autonomy
   • Even 30–50% task offload can be meaningful if it removes hazardous steps

What to watch in 2026

Seasonally, winter operations are a stress test: gloves, cold joints, condensation, and rushed response work. The robotics stacks that survive winter conditions (physically and operationally) are the ones worth scaling.

On the technology side, the biggest near-term improvements likely come from:

• better wrist force control and contact modeling
• richer tactile sensing that’s actually usable in training pipelines
• more reliable in-hand manipulation primitives
• autonomy stacks that can recover from failure without “freezing”

If you’re building a multi-year automation roadmap, plan for steady progress, not magic. The teams that win will be the ones that operationalize learning loops: deploy, measure, retrain, redeploy.

A better way to think about the “Humanoid Olympics” in utilities

Answer first: Treat these events as an evaluation rubric for real-world utility robotics, not as entertainment.

A robot that can open doors, handle tools, manage wet/dirty contact, and recover from mistakes is already most of the way to meaningful work in energy infrastructure.

If you’re leading AI in energy and utilities, you don’t need to bet on a single humanoid vendor today. You do need a clear standard for what “field-ready” means—and chores are a surprisingly good place to start. The question worth asking next isn’t whether robots can do household tasks.

It’s whether your organization is ready to adopt the operational discipline that makes robotics succeed: constrained workflows, measurable reliability, and a serious plan for safety and maintenance.