Humanoid Robot “Chores” That Predict Energy Automation

Robots can kickbox for a crowd, but that’s not the hard part. The hard part is opening a door without fumbling, manipulating a wet sponge without dropping it, or inserting a key while keeping the keyring in-hand.

That’s why Benjie Holson’s “Humanoid Olympics” challenge hit a nerve in robotics circles this fall: it replaces flashy demos with measurable, everyday manipulation milestones—doors, laundry, tools, fingertip work, and wet cleaning. If a robot can do those reliably, it’s not “good at chores.” It’s good at operating in the messy physical world.

If you work in energy and utilities, this matters more than it sounds. The same gaps Holson calls out—force feedback, touch, finger control, and precision—are exactly what stands between today’s automation and a future where AI-driven robots handle higher-risk field work: substation checks, valve operations, battery-room inspections, or storm-restoration tasks. Most companies get distracted by the humanoid form factor. The real signal is the capability stack.

The Humanoid Olympics is really a manipulation benchmark

Holson’s core point is simple: laundry-folding looks like general intelligence, but it’s a “sweet spot” task for current learning-from-demonstration techniques. You can record hundreds of short sequences, train a neural network to imitate, and get impressive videos.

But “sweet spot” doesn’t mean “solved,” and it definitely doesn’t mean “general.” The Humanoid Olympics is useful because it breaks manipulation into skill categories that transfer:

• Asymmetric forces (doors, latches, self-closing mechanisms)
• Deformable objects (shirts, socks, bags)
• Tool grasps (sprayers, knives, keys)
• In-hand manipulation (reorienting objects without setting them down)
• Wet/contaminated interaction (water, soap, grease, sticky substances)

In energy operations, those categories map cleanly to real work:

• Opening access panels in wind turbines or BESS enclosures
• Manipulating flexible hoses, seals, and cables
• Using torque tools, thermal cameras, test probes, and tag-out devices
• Turning knobs, aligning connectors, inserting fuses
• Dealing with rain, dust, oil mist, salt spray, mud, and grime

A good benchmark isn’t about spectacle. It’s about isolating the constraints that make automation fail in production.

What’s working now (and why it’s not enough)

Holson describes what many teams quietly rely on: learning from demonstration via teleoperation (duplicate robot “puppeteering” or VR controllers). It works well for 10–30 second skills repeated hundreds of times.

Then he lists the bottlenecks. For energy leaders evaluating robotics vendors, these are not academic footnotes—they’re procurement landmines:

1. No high-resolution force feedback at the wrists: without it, robots struggle with tight tolerances, binding mechanisms, and “feel-based” tasks.
2. Limited finger control: many systems are still closer to “open/close” than true dexterity.
3. No real sense of touch: human hands are sensor-dense; robots are not.
4. Medium precision: the practical precision often seen in the wild is roughly 1–3 cm.

That last one is especially relevant: 1–3 cm is fine for picking up a towel. It’s not fine for aligning a keyed connector, pressing a recessed reset, or inserting a locking pin.

Event 1 (Doors): a proxy for infrastructure access

Doors are the most honest test in robotics because they combine force, precision, and whole-body planning. Holson’s “boss fight” is a lever-handle, self-closing, pull door—because it demands you manage re-closing forces, handle torque, and body positioning.

In energy infrastructure, doors show up everywhere:

• Substation gates and equipment cabinets
• Battery storage containers (often with heavy latches and seals)
• Mechanical rooms with fire doors
• Offshore wind access hatches

Here’s the operational takeaway: If a robot can’t consistently get through access barriers, you don’t have an autonomous field worker—you have a lab demo.

Practical bridge to utilities

Utilities can use the “door test” mindset right now in pilot specs:

• Require interaction with real latches, not custom handles
• Include self-closing force or spring-loaded mechanisms
• Score success on “open → pass through → close/secure,” not just “handle moved”

That approach prevents a common trap: paying for a robot that can manipulate only the environment that was modified for it.

Event 2 (Laundry): deformables today, cables tomorrow

Laundry seems domestic, but the underlying challenge is deformable object manipulation. Shirts and socks behave like cables, tarps, insulation wraps, and flexible ducting: infinite shapes, partial occlusions, and frequent self-collisions.

Holson’s gold laundry event—hanging a men’s dress shirt and buttoning at least one button—forces two things that matter for industrial work:

• Two-handed coordination with deformables
• Small, high-precision, forceful actions (buttons)

In the energy world, “buttons” become:

• Cable clips
• Locking connectors
• Zip ties and pull tabs
• Protective caps and seals

If you’re building an automation roadmap for 2026–2028, deformables are where you should expect timelines to stretch. Most companies underestimate this. Rigid pick-and-place is one universe; deformables are another.

Event 3 (Tools): the real ROI layer for field robotics

Tools are where humanoids stop being “interesting” and start being economically inevitable.

Holson’s tool ladder goes from window cleaning, to making peanut butter sandwiches, to using a key without setting the keyring down. Those sound playful, but each maps to a serious operational competency:

• Independent finger actuation (trigger sprayers → torque triggers, deadman switches)
• Strong, stable tool grasp (knife spreading → scrapers, probes, torque tools)
• In-hand reorientation (selecting the right key → selecting the right bit/adapter)

For energy operators, the tool question is a procurement filter:

If your robot can’t use standard tools, every job turns into a custom end-effector project.

That’s how pilots die: the integration work swallows the business case.

A “tool-first” automation strategy that actually works

I’ve found that the fastest route to value is to standardize tools before you standardize robots. Examples:

• Create a “robot-ready” tool kit with known geometries and grasp points
• Use consistent lanyards, holsters, and placement zones
• Prefer tools with clear detents and alignment features

This isn’t dumbing down the problem. It’s engineering the interface layer so autonomy has a fighting chance.

Event 4 (Fingertips): why precision maintenance is still the wall

Fingertip manipulation is the dividing line between “mobile manipulator” and “general-purpose worker.”

Holson’s examples—rolling socks, opening a dog poop bag, peeling an orange—stress fine contact control and controlled slip. Robots struggle here because they lack tactile richness and because finger-level control multiplies complexity.

Utilities should care because fingertip work equals:

• Aligning and seating connectors
• Removing protective covers without dropping them
• Handling small fasteners in constrained spaces
• Managing fragile sensors or fiber connections

In other words: fingertip capability is what lets you automate tasks that currently require a skilled technician.

If you want a blunt forecast: until tactile sensing and dexterous hands mature, the highest-value tasks will remain human-led with robot assistance, not fully autonomous.

Event 5 (Wet manipulation): the messy reality of field work

“Wet manipulation” sounds niche until you list what field robots actually face:

• Rain, puddles, and spray
• Condensation in battery rooms
• Grease on mechanical linkages
• Mud during storm restoration
• Salt spray offshore

Holson’s wet events escalate from wiping a damp counter to washing grease off a pan in a sink. The deeper point is reliability: robots usually don’t like being wet, and many demos avoid contamination for good reason.

Energy operators should treat this as a safety-and-availability issue, not a feature checkbox. A robot that’s “water-resistant” but can’t be cleaned, decontaminated, or serviced quickly is a liability.

What to demand in pilots

If you’re trialing humanoid robots or mobile manipulators in 2026, add these requirements early:

• Defined ingress protection targets for hands and forearms
• A cleaning/decon procedure you can execute on-site
• Consumables plan (seals, glove covers, protective skins)
• Failure mode expectations: what happens after a slip into water?

What the Humanoid Olympics teaches utilities about AI

The Humanoid Olympics is framed as robotics, but the underlying lesson is about AI generalization under real constraints.

Energy AI has its own “laundry folding trap.” Predictive maintenance models can look great in a controlled dataset, then fail when:

• sensors drift,
• operating regimes change,
• rare events dominate risk,
• or the system encounters conditions the model never saw.

Robotic manipulation makes those weaknesses visible because the world pushes back—literally. The parallels are tight:

• Learning from demonstration ↔ learning from historical operations
• Force/touch limitations ↔ missing instrumentation and poor observability
• 1–3 cm precision ↔ error bars that are acceptable in dashboards but not in control loops
• Wet/dirty environments ↔ messy real-world data and domain shift

A strong stance: utilities should watch humanoid robotics not because they want humanoids everywhere, but because the research is stress-testing the same AI assumptions utilities rely on.

How to turn this into a 2026–2027 action plan

If you’re responsible for innovation, operations, reliability, or asset management, here’s a practical way to use these insights without chasing hype.

1) Build your “Infrastructure Olympics” scorecard

Create 5–10 tasks that look boring but are operationally real. Score them like Holson does: autonomous, no edits, real time constraints.

Examples:

• Open a standard substation cabinet, read a gauge, close and latch
• Pick up a dropped glove and place it in a disposal bin
• Turn a quarter-turn valve to a target position without overshoot
• Insert a standard test plug and confirm seated state
• Wipe and dry a condensation-prone sensor window without streaking

2) Separate perception wins from manipulation wins

Don’t let a strong vision model hide weak hands. Require demonstration of:

• contact stability,
• repeatability,
• recovery from slips,
• and safe shutdown on unexpected resistance.

3) Use robots as data collectors before they’re workers

The lowest-friction first deployment is often: robot as autonomous inspector, not autonomous mechanic.

• thermal scans
• acoustic anomaly sweeps
• visual corrosion checks
• meter reading with cross-validation

Then you graduate to light manipulation.

Where this goes next

Humanoid robots doing chores isn’t the finish line. It’s the most brutally clear way to measure whether AI-driven automation can handle the physical world.

For energy and utilities, the near-term win isn’t “a robot that does everything.” It’s a pipeline: inspection → assisted manipulation → limited-scope autonomous intervention. The Humanoid Olympics helps you see which vendors and approaches are actually moving up that ladder.

If a robot can open doors, use tools, handle deformables, and survive wet, dirty conditions, it’s not learning to make sandwiches. It’s learning to operate infrastructure.

What’s one operational task in your environment that sounds simple, but breaks every automation attempt the moment the real world shows up?