Humanoid Robot Skills That Utilities Actually Need

Most robotics demos are still built to impress other roboticists, not the people who’d pay for robots at scale. A humanoid doing choreographed punches is fun, but it doesn’t help a utility crew restore service faster, reduce truck rolls, or keep workers out of hazardous environments.

Benjie Holson’s “Humanoid Olympics” challenge list is a better yardstick because it’s grounded in manipulation tasks that normal humans do without thinking—doors, tools, wet cleanup, fingertip work. Here’s the energy-and-utilities angle: those “mundane” tasks map surprisingly well to the physical realities of substations, generation plants, water utilities, and field maintenance. If a robot can’t manage asymmetric forces on a door handle, it won’t reliably handle a stuck disconnect switch, a cabinet latch, or a gasketed enclosure.

This post is part of our AI in Robotics & Automation series, and it takes a stance: utility robotics won’t scale on flashy autonomy claims. It’ll scale on measurable, repeatable physical competencies—tested like a sport, procured like industrial equipment, and integrated like a safety system.

Why “chore robots” are a proxy for utility robots

Utilities don’t need robots that look human. They need robots that can operate in human-built environments with inconsistent hardware, weather, grime, and imperfect documentation.

Holson’s events look like household chores, but they’re really a catalog of robot manipulation primitives:

• Forceful interaction without slipping (door handles, valves, stuck parts)
• In-hand manipulation (keys, small fasteners, connectors)
• Compliance and precision (aligning, seating, turning)
• Wet/dirty robustness (water, grease, soap, de-icing fluids)
• Whole-body coordination (moving through constrained spaces)

In the field, those primitives show up everywhere: opening pad-mounted gear, manipulating insulated tools, wiping lenses on inspection cameras, cleaning a sensor faceplate, or operating a lockout/tagout hasp.

The important point isn’t “humanoids will replace lineworkers.” The point is: AI-driven robotics is finally good enough to attempt real operational tasks—if we choose the right benchmarks.

The hidden constraint: today’s training methods

A lot of the recent progress in robot manipulation comes from learning from demonstration—teleoperating a robot (often via VR or a “twin robot” puppeteering setup), recording hundreds of short trials, and training a neural policy to imitate.

That approach works well when:

• The task is short (10–30 seconds)
• The environment can be controlled
• Failures are recoverable
• Precision requirements are modest

Utilities should pay attention because this is also how many AI systems mature in grid and asset analytics: start with narrow, high-value use cases, then expand. But robotics adds a hard twist: physics and contact.

Holson calls out a set of limitations that matter directly for energy operations:

• Weak force feedback at the wrists
• Limited finger dexterity compared with human hands
• Little to no touch sensing that’s usable in real-time
• Medium precision (often centimeters, not millimeters)

If you’re thinking “we can just add more cameras,” you’re not wrong—but you’re also not done. Visual perception helps; it doesn’t replace tactile sensing when you’re seating a connector, aligning a keyway, or twisting something that fights back.

Event 1: Doors → cabinets, access control, and compliance

Answer first: Doors are a stress test for contact-rich manipulation with asymmetric forces, and utilities face door-like problems constantly.

Holson’s door progression—round knob push door, lever handle self-closing, then lever handle self-closing pull door—sounds trivial until you try to do it autonomously with a robot that has imperfect perception and limited touch.

For utilities, the analogy isn’t a front door. It’s:

• Substation and plant enclosure doors with gasket friction
• Padlocks and latches (often stiff, corroded, or iced)
• NEMA cabinets where you must pull while controlling swing
• Self-closing access points in secure facilities

What makes it hard is exactly what makes many utility tasks hard:

1. High torque + low slip margin. You need strong twisting without losing the grasp.
2. Constraint-aware pulling. Pulling off-axis causes slip and impacts.
3. Whole-body coordination. The robot has to reposition while maintaining control.

What “good” looks like for utility procurement

If you’re piloting field robotics, don’t accept “it opened the door once.” Ask for:

• No-cut, real-time video across multiple door variants
• Success under misalignment (handle height/angle changes)
• Performance after light contamination (dust, drizzle, glove-like covers)
• Recovery behavior: what it does after a partial slip

This is where a sports-style benchmark helps. You can write it into acceptance testing.

Event 3: Tools → the real bottleneck for utility automation

Answer first: Tool use is where “general-purpose” robots either become economically useful or stay in the lab.

Humans don’t apply strength directly; we apply it through tools. Utility work is tool-heavy by design: insulated sticks, torque wrenches, crimpers, spray bottles, gauges, and cleaning implements.

Holson’s sequence—spray bottle + paper towels, peanut butter sandwich, then using a key—maps cleanly:

• Spray bottle → applying cleaners, leak-detection fluids, de-icing spray, or marking paint
• Spreading task → controlled force along a path (sealant, thermal paste, cleaning buildup)
• Key use → in-hand manipulation and precise alignment under force

The “key” challenge is particularly revealing. It combines:

• Selecting the right item from a cluster (keyring-like clutter)
• Reorienting it without setting it down
• Inserting with tight tolerances
• Turning while maintaining axial engagement

In the utility world, swap “key” with:

• A test probe into a port
• A connector into a receptacle
• A specialty wrench onto a fastener
• A fuse puller engaging a handle

If a vendor claims “autonomous tool use,” ask a blunt question: Can it regrasp and reorient in-hand, or does it rely on the world as a fixture? Many systems quietly depend on carefully staged fixtures. That can still be useful—but it’s not general-purpose, and your operations team should know the difference.

Event 5: Wet manipulation → what separates pilots from deployments

Answer first: Wet, dirty, and greasy manipulation is the difference between a demo and a real utility workflow.

Utility environments are rarely dry and clean:

• Condensation inside cabinets
• Rain and wind-driven spray
• Grease on mechanical linkages
• Mud, salt, or soot on surfaces
• Cleaning workflows after maintenance

Holson’s wet tasks—wiping a countertop with a sponge, cleaning peanut butter off the manipulator, washing grease off a pan—sound domestic. But they represent a core operational requirement: the robot must tolerate and function in messy conditions, then return to service.

Here’s the operational reality I’ve seen trip teams up: if a robot gets dirty and you need a technician to clean it every hour, your “autonomy” just became a new kind of labor.

A practical standard utilities can adopt

When you run a robotics proof of value, add a “mess budget”:

• The robot must complete the task after exposure to water spray, mild soap, and light grease
• It must demonstrate self-cleaning of critical contact surfaces (grippers, cameras) or easy field servicing
• It must show ingress protection strategy for hands/end effectors (not just the torso)

This isn’t about being harsh. It’s about preventing a common failure mode: robots that only work in curated conditions.

What utilities can learn from the Humanoid Olympics model

Answer first: The best way to accelerate AI-driven robotics in utilities is to turn “requirements” into repeatable, public benchmarks.

Holson’s rules—autonomous, real-time video, no cuts, time limits—are exactly the kind of constraints that prevent benchmark gaming. Utilities can borrow the structure without copying the tasks verbatim.

A utility-ready “Manipulation Decathlon” (starter set)

If you want to evaluate AI robotics vendors (or your own internal program), these tests are a strong starting point:

1. Open/close a gasketed enclosure with a latch, then re-latch it
2. Operate a quarter-turn valve with a torque threshold and stop condition
3. Insert and remove a connector with alignment tolerance and verification
4. Pick up and use a handheld tool (sprayer or torque tool) with measured outcome
5. Wipe and inspect: clean a lens/cover and capture an inspection image

Each test should define:

• The allowed time (e.g., 10× skilled human time)
• Pass/fail criteria (no “mostly works”)
• Variations (at least 3 hardware variants)
• Safety constraints (force limits, emergency stop behavior)

Why this ties back to AI in energy operations

AI in utilities often starts in software: load forecasting, outage prediction, asset health analytics. That’s valuable, but it still leaves a gap between “we detected a problem” and “we fixed it.” Robotics is how you close that loop.

Think of it as a stack:

• AI for prediction: find failing assets earlier
• AI for planning: schedule crews, stage parts, route trucks
• AI-driven robotics: execute repeatable physical work safely

When the manipulation layer improves (force control, touch sensing, dexterous grasping), it doesn’t just help household chores. It enables safer, faster response in the field.

People also ask (and the straight answers)

Are humanoid robots the right form factor for utilities?

Sometimes. Humanoid form factors help when the environment is built for humans (stairs, doorways, standard cabinets). But many utility tasks are better served by mobile manipulators, tracked platforms, or specialized end effectors. The capability matters more than the silhouette.

Why not automate utilities with fixed robots instead?

Fixed automation works in plants with stable layouts. The field is different: variability is the norm. General-purpose robots win when the cost of re-engineering every site is higher than improving the robot’s adaptability.

What’s the biggest technical gap right now?

Tactile sensing and force-aware control that’s robust in messy conditions. Vision-only manipulation hits a wall when you need reliable insertion, turning, and high-force contact without slip.

Where this goes next for AI-driven robotics in utilities

The fastest path to real value isn’t waiting for a perfect humanoid that can do everything. It’s building toward task competence the way Holson proposes: clear events, strict validation, and incremental medals.

If you’re leading innovation in an energy or utility organization, the next step is practical: pick one manipulation-heavy workflow that drives cost and risk—cabinet access, basic tool use, wet cleaning for inspection readiness—and define a benchmark your team can run every quarter. Then measure progress like an operations metric, not a science project.

The forward-looking question worth asking in 2026 budget planning is simple: When our AI detects an issue in the grid, how many steps are still “human-only” before it gets fixed—and which of those steps could a robot reasonably earn a medal in first?