The future of repair: physical AI

Robots are learning skilled work. Vehicle repair is next.

Physical AI is AI that perceives, decides and acts in the physical world through robots. Robots already change tyres, plug in charging cables and repair bumpers, and humanoids are learning factory work from video of skilled people. The next frontier is automotive repair, maintenance and service, and it needs real repair data to get there. That is the data AMPS captures.

Beyond the factory

Physical AI is spreading to every skilled task.

AP describes physical AI as machines with AI and sensors that perceive, decide and act in real-world environments. Wherever work depends on perception, hand-eye coordination and judgement, physical AI needs real-world data to learn from. It is moving from factory floors into the field and emergency response. Automotive repair, maintenance and service is next.

PerceptionSeeing the taskRecognising parts, tools, damage and condition, as a skilled person does.
Hand-eye coordinationMoving with precisionGrip, angle, force and sequence, learned from real demonstrations.
JudgementDeciding what to doChoosing the next step, checking the result and handling the unexpected.
WorkplacesSouth Korea · May 2026
Skilled motions recorded to build robot "brains" RLWRLD fits workers with head, chest and hand cameras. Their motions, including joint angles and force, feed a database that trains robots. AP calls this physical AI: machines that perceive, decide and act in the real world. Source: AP via WTOP ↗
Field roboticsUnited States · Sep 2026
Robots that keep learning after deployment Skylark Labs, with researchers from Carnegie Mellon and UC Berkeley, developed Continual Field-Adaptive Models. Manipulators, humanoids, quadrupeds, drones and off-road vehicles keep learning from real-world experience without retraining their core models. Source: Robotics & Automation News ↗
Emergency responseUnited States · Research
Robot fleets that find and assess casualties In the DARPA Triage Challenge, university teams such as the University of Pennsylvania build ground and aerial robots that locate and assess casualties for first responders, under real-world conditions. Source: University of Pennsylvania ↗

Where AMPS comes in: AMPS captures this kind of data in automotive workshops, during real repair, maintenance and service work, then anonymises and structures it so physical-AI models can learn skilled repair.

Where repair is heading

Three stages towards the robot-assisted workshop.

This is the AMPS view of how automation will reach vehicle maintenance. It is an outlook, not a forecast with dates. Each stage builds on data from the one before.

Stage 1 · Now Assisted and narrow automation Robots take single, well-defined tasks; AI supports diagnosis. Skilled technicians do the repair.
→Tyre and wheel robots→Charging robots→AI-supported diagnostics
Stage 2 · Next Robots learn from recorded repairs Models trained on video and motion data of real repairs begin to assist with repetitive workshop tasks.
→Learning from demonstration→Service and fast-fit tasks first→Technicians supervise
Stage 3 · Future The robot-assisted workshop Robots handle a growing share of standard maintenance; technicians lead diagnosis, quality and complex repair.
→Broader task coverage→Human-robot teams→Data-driven repair standards
Repair robotics today

Robots are already working on vehicles.

Physical AI already handles narrow, well-defined jobs on vehicles: wheels, charging, bumper repair and AI-supported diagnostics. Each one shows where broader repair automation can start.

TyresUnited States
Robots that remove, lift and fit wheels RoboTire systems at Discount Tire stores handle the wheel work while technicians operate them. The company says it cuts a roughly hour-long job to under 25 minutes. Source: Dallas Innovates ↗
Smart repairGermany
An autonomous robot for bumper repair Fraunhofer IWU and IST’s Auto²ReBot finds the damage, removes material, prints new material and repaints. Source: Fraunhofer IWU ↗
EV chargingGermany
A mobile robot that plugs in the car Volkswagen showed a six-wheeled prototype in Dresden with a robotic arm that can connect the charging cable to a vehicle on its own. Source: electrive ↗
DiagnosticsGermany
Diagnostic AI trained on real workshop data In the federally funded "Autowerkstatt 4.0" project (2022–2024, partners including DEKRA, LMIS and DFKI), workshops across Germany were invited to collect real measurement data to train machine-learning models for AI-supported diagnostics. Source: Autoservicepraxis ↗
How robots learn manual skills

They watch skilled hands, then practise.

Manual work is hard to program step by step. Increasingly, robots learn it from demonstration instead: video and motion data of a skilled person doing the task, turned into a model that predicts the right movement in each situation. The quality and variety of those demonstrations decide how well the robot performs.

01 MovementHow hands and tools move: grip, angle, force, sequence.
02 PerspectiveFirst-person video showing what the technician sees.
03 TimingHow long each step takes in real conditions.
04 ContextWhich vehicle, which fault, which decision, which check.
Already happening Germany · Jul 2026 ↗ Tesla records Grünheide workers to train Optimus Cameras capture how employees grip tools, handle parts and carry out steps. (electrive, citing Handelsblatt) Germany · Jul 2026 ↗ Audi robots learn dexterous tasks from video Mimic Robotics’ video-action model learns work such as car-door assembly from demonstrations. (Robotics 24/7) Germany · Feb 2026 ↗ BMW brings humanoid robots to its Leipzig plant After a Figure 02 humanoid supported production of 30,000+ BMW X3s in the US, BMW is piloting humanoids in Germany. (BMW Group) South Korea · May 2026 ↗ Workers wear head, chest and hand cameras to teach robots RLWRLD records skilled staff at work and turns their motions, including joint angles and force, into robot training data. (AP via WTOP)
The missing ingredient

Factories record their work. Workshops don't, yet.

A car factory repeats the same steps on the same model thousands of times. A workshop sees a different vehicle, fault and condition in every bay. That variety is exactly what a repair robot must learn, and it is rarely recorded anywhere.

The gap Every vehicle is different Brands, models, ages and conditions vary in every bay. Robots must learn that variety.
What AMPS captures Repairs recorded across brands, repair areas and workshops.
The gap Know-how lives in people How an experienced technician approaches a job is rarely written down.
What AMPS captures First-person video of hand-eye coordination and key decisions.
The gap Privacy is non-negotiable Workshop footage contains people, plates and customer details.
What AMPS captures Data anonymised before processing, with clients informed upfront.
Which repair tasks come first

Not every job automates at the same pace.

Our view on how the main repair areas line up for automation. The more repetitive and standardised the task, the sooner robots can assist. The more judgement it needs, the longer people lead and AI advises.

Repair areaAutomation outlookLikely first stepData that helps most
Tyres & fast-fit Early Wheel removal, lifting and fitting Wheel handling, cycle times
EV & high-voltage Early (charging) Automated charging connection Safety routines, battery handling
Service & maintenance Emerging Repetitive service steps Task sequences, tool handling, timing
Body, paint & smart repair Research Defined repairs such as bumpers Fine-motor technique, surface prep
Diagnostics & ADAS AI-assisted AI suggests, technician decides Fault-tracing paths, decision points
Truck, bus & heavy Longer term Assisted handling of heavy parts Heavy-part handling, multi-person flows
Outlook reflects AMPS's assessment, not a prediction with fixed timelines.
People and robots

Robots take the strain. Technicians keep the judgement.

In the deployments that exist today, robots take on the heavy, repetitive part of a job while technicians operate them and handle the rest. At Discount Tire stores, for example, RoboTire systems remove, lift and fit wheels while technicians operate them. We expect the workshop of the future to work the same way: robots for repetition and load, people for diagnosis, quality and the unexpected.

RobotsRepetitionThe same movement, many times a day.
RobotsPhysical loadLifting wheels, holding heavy parts.
TechniciansDiagnosisFinding the real cause of a fault.
TechniciansQuality & exceptionsChecking the result and handling the unexpected.
Rolled out through dealer networks

Today's repair data. Tomorrow's physical-AI aftersales.

OEMs and importers can roll the AMPS programme out through their dealer networks; large dealer groups can run it across their sites. The network's repair, maintenance and service work becomes the data physical AI learns from, and the repair work is completed along the way.

Request a programme proposal → Automation or AI partner? Talk to us → Links on this page lead to independent sources. AMPS is not affiliated with the companies and institutes mentioned.
Repair Robotics & the Future of Repair | AMPS
The future of repair: physical AI

Robots are learning skilled work. Vehicle repair is next.

Physical AI is AI that perceives, decides and acts in the physical world through robots. Robots already change tyres, plug in charging cables and repair bumpers, and humanoids are learning factory work from video of skilled people. The next frontier is automotive repair, maintenance and service, and it needs real repair data to get there. That is the data AMPS captures.

How robots learn repair → The AMPS programme →
Beyond the factory

Physical AI is spreading to every skilled task.

AP describes physical AI as machines with AI and sensors that perceive, decide and act in real-world environments. Wherever work depends on perception, hand-eye coordination and judgement, physical AI needs real-world data to learn from. It is moving from factory floors into the field and emergency response. Automotive repair, maintenance and service is next.

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Where AMPS comes in: AMPS captures this kind of data in automotive workshops, during real repair, maintenance and service work, then anonymises and structures it so physical-AI models can learn skilled repair.

Where repair is heading

Three stages towards the robot-assisted workshop.

This is the AMPS view of how automation will reach vehicle maintenance. It is an outlook, not a forecast with dates. Each stage builds on data from the one before.

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Repair robotics today

Robots are already working on vehicles.

Physical AI already handles narrow, well-defined jobs on vehicles: wheels, charging, bumper repair and AI-supported diagnostics. Each one shows where broader repair automation can start.

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How robots learn manual skills

They watch skilled hands, then practise.

Manual work is hard to program step by step. Increasingly, robots learn it from demonstration instead: video and motion data of a skilled person doing the task, turned into a model that predicts the right movement in each situation. The quality and variety of those demonstrations decide how well the robot performs.

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The missing ingredient

Factories record their work. Workshops don't, yet.

A car factory repeats the same steps on the same model thousands of times. A workshop sees a different vehicle, fault and condition in every bay. That variety is exactly what a repair robot must learn, and it is rarely recorded anywhere.

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Which repair tasks come first

Not every job automates at the same pace.

Our view on how the main repair areas line up for automation. The more repetitive and standardised the task, the sooner robots can assist. The more judgement it needs, the longer people lead and AI advises.

Repair areaAutomation outlookLikely first stepData that helps most
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Outlook reflects AMPS's assessment, not a prediction with fixed timelines.
People and robots

Robots take the strain. Technicians keep the judgement.

In the deployments that exist today, robots take on the heavy, repetitive part of a job while technicians operate them and handle the rest. At Discount Tire stores, for example, RoboTire systems remove, lift and fit wheels while technicians operate them. We expect the workshop of the future to work the same way: robots for repetition and load, people for diagnosis, quality and the unexpected.

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Rolled out through dealer networks

Today's repair data. Tomorrow's physical-AI aftersales.

OEMs and importers can roll the AMPS programme out through their dealer networks; large dealer groups can run it across their sites. The network's repair, maintenance and service work becomes the data physical AI learns from, and the repair work is completed along the way.

Request a programme proposal → Automation or AI partner? Talk to us → Links on this page lead to independent sources. AMPS is not affiliated with the companies and institutes mentioned.