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High-quality real-world data has been a major bottleneck for teaching robot hands: A video can show movement without measuring touch or force. Building on our robotics inflection point and September AI and robotics developments, advances in sensors, computing and models are turning richer demonstrations into robot-training data, which could enable an entirely new class of automation and skills in previously inaccessible domains. VettaFi research suggests that solving the robotic hand — across its many sizes, forms, and uses — could expand robotics’ total addressable market by trillions of dollars over the coming decades.
Key Takeaways
- Everyday human activity is becoming robot-training data. Paid programs recruit people to record cooking, laundry and tool use, while industrial systems capture skilled work. Together, video, motion, and touch form multimodal data that can help create more informed and capable AI models.
- Better manipulation could expand automation across homes, services, factories, and infrastructure. Different jobs can call for micro hands (e.g., small electronics assembly), human-scale hands and extremely durable, outdoor-capable hands. Beyond humanoids, robot hands can be added as upgrades to existing robots and production lines.
- The investment opportunity reaches from model training and simulation to sensing and low-power processing near the task. For example, Ambiq Micro, Inc.
(AMBQ), added to THNQ Artificial Intelligence Index at its most recent rebalance in Q3 2026, illustrates the potential for AI to spread into smaller physical devices and wearable technologies.
How Robot Hands Sense, Move, and Use Power
Robot hands will range from micro grippers for tiny components to human-scale hands and heavy-duty industrial grippers. They can work on fixed arms, automated rails, and production lines. Meanwhile, future tool-equipped aerial manipulators could help maintain grid infrastructure, wind turbines or hard-to-reach building equipment.
Infineon Technologies AG
(IFX:ETR) is adapting sensor technologies already used in smartphones and household appliances for robotic hands. At its October 16 presentation at SEDEX 2026, the company outlined magnetic, pressure and capacitive sensing approaches for detecting contact and controlling grip. It is also exploring tiny microelectromechanical systems (MEMS) microphones to identify characteristics of objects through the sounds and vibrations produced by touch. That could give a hand additional information about what it is handling.
The company’s approach also connects sensing with movement: Microcontrollers and motor drivers coordinate finger joints, while feedback helps adjust gripping force. Infineon supports centralized, hybrid and distributed control architectures, allowing designers to place control electronics differently as hands become more complex. Its gallium nitride power technology targets smaller, more efficient motor-drive circuits, addressing the challenge of fitting power and control close to moving joints.
Separately, Novanta Inc.
(NOVT), through ATI, supplies force/torque sensors that measure pushes, pulls, and twisting.
Actuation turns electrical power into movement through motors and mechanical transmissions. Motor drivers meter that power, while feedback helps regulate position and grip.
Microchip Technology Incorporated
(MCHP) also targets humanoid hands with programmable chips that coordinate multiple motors and sensor streams. Together, sensing, control, and power electronics help connect a model’s instructions to physical action.
The difficulty is substantial. In an August 2024 interview, Tesla, Inc.
(TSLA) CEOElon Musk said: “From an electromechanical standpoint, the hand is probably roughly half of the engineering.”
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Solving the Real-World Data Bottleneck for Robot Hands
There is plenty of online video of someone folding laundry or using a drill. However, ordinary footage does not directly measure grip pressure, resistance or contact. Together, motion gloves, pressure sensors, and head-mounted cameras can help capture those complementary signals: multimodal data about the same task.
Consequently, collecting contact-rich demonstrations requires sensing alongside video. Sensors must capture relevant signals, while the data pipeline aligns them with movement and video. Different people, objects and environments add further complexity.
As an example, private provider Micro1 recruits people globally for paid recordings of cooking, laundry, and tool use. Meanwhile, MANUS has demonstrated a research integration combining gloves, body tracking, and a head-mounted camera.
After capture, developers synchronize signals and label useful examples. They then combine real and simulated data, train models, and test them on machines.
Nvidia Corporation
(NVDA) supplies GPU computing hardware alongside robotics software. Isaac Sim simulates robots and generates training data; Isaac Lab supports learning from demonstrations and simulated practice. Physical testing then establishes whether those skills work on real machines.
How Can Manufacturers Integrate Specialist Robot Hands?
Manufacturers can combine specialist suppliers with their own engineering, even if they are not the ones developing the hands themselves. For example, ABB Ltd.
(ABBN:SWX) announced a June 2026 collaboration integrating private developer Psyonic’s touch-sensitive Ability Hand with its GoFa collaborative robot. The partners are testing human prosthetic-use data for teaching delicate, variable handling.
Hon Hai Precision Industry Co., Ltd.
(2317:TAI) (DBA Foxconn) provides another industrial development example. As a blue-chip electronics manufacturer that partners with companies like Apple Inc.
(AAPL) and Nvidia to help make iPhones and AI data-center hardware, Foxconn is starting to pilot robotic-hand development alongside industrial training. In March 2026, its Nvidia collaboration included humanoid picking, placing, and screw fastening, combining simulated factory scenarios with on-site iteration.
Meanwhile, Universal Robots, owned by Teradyne, Inc.
(TER), launched its AI Trainer with Scale AI in March 2026. Operators guide robots while the system records synchronized motion, force, and video.
These approaches let manufacturers test capabilities without developing every component themselves. If reliable robot hands expand existing equipment’s tasks, operating leverage could come through higher utilization, fewer interventions and lower costs per job. The market is not fully appreciating this upside for the ecosystem.
Physical AI: Investment Implications and the Bottom Line
A wearable device also needs to process signals within limits on battery life, weight and heat. Ambiq, added to THNQ at its most recent rebalance in Q3 2026, supplies low-power processors for wearables and industrial sensors. Its SEC filings name Garmin Ltd.
(GRMN) and Alphabet Inc.
(GOOGL), through Google, among its end customers. Even Realities’ G2 smart glasses also run on its Apollo510 chip. Ambiq lists industrial edge and robotics among target uses for its newer Atomiq chip. We increasingly expect physical AI applications, including robot hands, to follow.
This is AI spreading down the stack: Larger systems train models, while smaller devices collect signals and run selected functions near the task. Companies across the ROBO Global Robotics & Automation Index(ROBO)
participate through sensing, actuation, robot manufacturing and integration. THNQ connects training infrastructure with the computing that brings AI into smaller physical devices.
The opportunity extends to manufacturers adopting these technologies, where better manipulation could expand what existing equipment can do. As hands become more capable, durable and economical, automation can reach more work across homes, services, factories and infrastructure.
For scale, Morgan Stanley estimated in May 2025 that the humanoid market alone could surpass $5 trillion by 2050. The commercial impact could spread through both the companies supplying those capabilities and the businesses putting them to work.
The ROBO Global Robotics and Automation Index underlies the ROBO Global Robotics and Automation Index ETF
(ROBOB). The ROBO Global Artificial Intelligence Index underlies the ROBO Global Artificial Intelligence ETF
(THNQB-). It is not possible to invest directly in an index.
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VettaFi LLC (“VettaFi”) is the index provider for ROBO and THNQ, for which it receives an index licensing fee. However, ROBO and THNQ are not issued, sponsored, endorsed, or sold by VettaFi, and VettaFi has no obligation or liability in connection with the issuance, administration, marketing, or trading of ROBO and THNQ.
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