Faraday Future is expanding its presence in robotics with new deployments of humanoid systems in both commercial and educational settings, as the electric vehicle maker pushes deeper into what it calls an “embodied AI” ecosystem.

The California-based company said it recently delivered two of its robots – the Master and Aegis models – to Los Angeles-based New PBB Auto, where they will be used for reception and customer-facing duties in dealership and showroom environments.

The move reflects an emerging strategy among robotics companies to introduce humanoid systems into service roles where interaction with customers is as important as physical capability.

At the same time, Faraday Future is testing how its robots can be used in education, pointing to a broader effort to position embodied AI as both a commercial tool and a learning platform.

From Showroom Assistants to Service Platforms

The initial deployment focuses on dealership operations, where the robots are expected to greet visitors, provide information and assist with basic customer service tasks.

Such roles are increasingly seen as an early entry point for humanoid robots. Unlike industrial environments, where precision and speed dominate, service settings require machines to interact naturally with people – an area where embodied AI systems are still evolving.

Faraday Future’s approach suggests that automakers may view robotics as an extension of their existing ecosystems, connecting vehicles, retail environments and digital services.

The delivery is tied to a broader business relationship with New PBB Auto, which has previously committed to future vehicle orders from the company.

Testing Robots as Educational Tools

Alongside commercial deployments, Faraday Future has begun experimenting with robotics in education.

In Los Angeles, the company hosted an interactive demonstration involving more than 300 students, where participants engaged directly with humanoid robots, a robotic dog and one of the company’s vehicles.

The event, held in collaboration with a local school district, emphasized hands-on interaction rather than passive demonstrations. Company representatives said the experience highlighted how direct engagement with robots can increase student interest in artificial intelligence and STEM fields.

Faraday Future is now exploring whether such programs could be scaled into structured educational offerings, potentially combining robotics demonstrations with curriculum development.

The company describes this approach as a “Robot & Vehicle + Education” model, in which embodied AI systems are used not only as tools but also as teaching platforms.

Automakers Enter the Robotics Race

Faraday Future’s expansion into robotics underscores a broader trend in the automotive industry.

As vehicles become increasingly software-driven, automakers are exploring adjacent markets where artificial intelligence and hardware integration intersect. Robotics – particularly humanoid systems – represents one such opportunity.

Companies including Tesla have already signaled ambitions to develop humanoid robots alongside their automotive businesses. Faraday Future’s approach suggests a different entry point, focusing initially on service and education rather than large-scale industrial deployment.

The strategy also reflects a key challenge facing the robotics industry: identifying practical, near-term use cases.

While humanoid robots are often associated with future household or industrial roles, many companies are beginning with smaller, controlled deployments in environments such as retail, hospitality and education.

For Faraday Future, the combination of dealership deployments and school programs provides an early test of how embodied AI systems can function in real-world settings.

Whether these applications scale into a broader business remains uncertain. But the company’s efforts highlight how robotics is increasingly being treated not as a standalone industry, but as part of a wider ecosystem connecting mobility, services and digital intelligence.

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A new robotics system developed by researchers aims to solve one of imitation learning’s most persistent limitations: speed.

The approach allows robots to perform tasks significantly faster than the humans who trained them, while maintaining precision and control. The advance could accelerate the adoption of robots in industries where efficiency and throughput are critical, from manufacturing to food preparation.

Imitation learning – where robots learn by observing human actions – has become a central method in modern robotics. But until now, robots have largely been constrained by the pace of human demonstrations, limiting their usefulness in real-world applications.

The new system, called SAIL (Speed Adaptation for Imitation Learning), is designed to break that constraint.

Breaking the Human-Speed Barrier

Imitation learning has gained traction because it allows robots to acquire complex, humanlike behaviors without requiring engineers to manually program every movement.

Tasks such as folding laundry, arranging objects or handling food are difficult to encode explicitly but relatively easy for humans to demonstrate. By observing these demonstrations, robots can learn to replicate them.

The challenge has been that robots typically execute these learned behaviors at roughly the same speed as the original demonstrations. Attempting to move faster can introduce instability, reduce accuracy or cause failures when environmental conditions change.

SAIL addresses this by introducing a system that adapts motion speed dynamically while preserving stability.

The approach combines multiple components that manage motion smoothness, track positioning, adjust execution speed based on task complexity and account for hardware limitations. Together, these elements allow robots to accelerate beyond their training data without losing control.

Toward Practical, General-Purpose Robots

In testing, robots using SAIL completed a range of tasks – including stacking objects, folding cloth and preparing food – up to three to four times faster than conventional imitation-learning systems.

The results suggest that robots can exceed human speed while maintaining the level of precision required for real-world work.

At the same time, the system is designed to recognize when slowing down is necessary. Tasks that require sustained contact or delicate handling may still benefit from reduced speed to avoid errors.

This balance between speed and control reflects a broader challenge in robotics: building systems that are not only capable but also reliable under varying conditions.

Researchers say the goal is not simply faster robots, but systems that can intelligently adapt their behavior depending on the task.

Closing the Gap Between Research and Deployment

The development highlights a key transition underway in robotics.

While imitation learning has enabled impressive demonstrations in laboratory settings, deploying such systems in real-world environments requires meeting stricter demands for speed, consistency and robustness.

Industrial applications, in particular, require robots to operate at a pace that justifies their cost while maintaining high levels of accuracy.

By allowing robots to learn from human demonstrations but operate beyond human speed, SAIL could help bridge the gap between research prototypes and commercial systems.

The work also points toward a broader ambition in robotics: creating general-purpose machines capable of performing a wide range of tasks that human hands can do.

Achieving that goal will require not only learning from humans, but also surpassing human limitations where efficiency demands it.

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China has released its first industry standard for embodied artificial intelligence, signaling a shift from experimental development toward formalized evaluation and deployment of physical AI systems.

The standard, developed by the China Academy of Information and Communications Technology in collaboration with more than 40 institutions, introduces a unified framework for benchmarking and testing embodied AI technologies. It is scheduled to take effect on June 1, 2026.

The move reflects growing efforts to define how robots and AI systems operating in the physical world should be measured, compared and validated as the sector expands rapidly.

Establishing Benchmarks for Physical AI

Unlike traditional software-based AI, embodied intelligence involves systems that must perceive, decide and act within real-world environments. Measuring performance in such systems has historically been difficult, with companies relying on proprietary metrics or isolated testing scenarios.

The new standard aims to address that gap by defining consistent evaluation methodologies and capability requirements across the industry.

It outlines how embodied AI systems should be assessed in areas such as perception, motion control and interaction with environments. It also introduces guidance on system architecture, helping standardize how hardware and software components are integrated.

By creating a shared set of benchmarks, the framework is expected to make it easier to compare different technologies and accelerate adoption in industrial and commercial settings.

From Research to Industrialization

The release of the standard builds on earlier efforts by China’s Ministry of Industry and Information Technology, which published a broader framework for humanoid robots and embodied intelligence earlier this year.

Together, these initiatives suggest that China is moving to formalize the technical foundations of the sector as it transitions from research to large-scale deployment.

Standardization has historically played a critical role in scaling emerging technologies. In industries such as telecommunications and manufacturing, common standards have enabled interoperability, reduced uncertainty and encouraged investment.

For embodied AI, the introduction of shared evaluation criteria could help companies move beyond isolated pilot projects and toward more widely deployable systems.

A Strategic Push in the Global Robotics Race

China’s move comes amid intensifying global competition in robotics and artificial intelligence.

Governments and companies are increasingly viewing embodied AI as a strategic technology with implications for manufacturing, logistics, healthcare and national security.

By establishing early standards, China may be positioning itself to influence how the global industry defines performance and safety benchmarks for humanoid robots and other physical AI systems.

The challenge for the industry will be ensuring that such standards remain flexible enough to accommodate rapid technological advances while providing meaningful guidance for deployment.

As embodied AI systems move closer to real-world adoption, the question is no longer only how to build capable robots, but also how to measure their performance in consistent and reliable ways.

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SAIC-GM has deployed humanoid robots on a battery assembly line for Buick vehicles, marking one of the first known integrations of embodied intelligent machines into automotive production in China.

The robots, developed jointly with Shanghai-based startup Agibot, are currently operating on the production line for the Buick Electra E7, a plug-in hybrid SUV scheduled for release this year. The deployment represents a pilot phase as the automaker evaluates how humanoid systems can function within existing manufacturing workflows.

Unlike traditional industrial robots, which are typically fixed in place and designed for repetitive tasks, the new system introduces a more flexible, mobile form of automation.

A Different Approach to Factory Robotics

The robot, named Nengzai No. 1, uses a wheeled base rather than legs, allowing it to move efficiently across the factory floor while maintaining stability for precision tasks.

Equipped with dual robotic arms and a visual perception system, it can identify battery components, plan grasping paths and execute loading operations without relying entirely on pre-programmed sequences.

SAIC-GM says the robot achieves positioning accuracy within 0.1 millimeters, while operating at a pace of roughly two seconds per task, aligning with the speed requirements of a mass-production environment.

The system also occupies significantly less space than conventional automated workstations, using less than 15 percent of the typical footprint. This compact design allows manufacturers to increase production density without expanding factory floor space.

The decision to deploy the robots on battery assembly lines reflects the complexity and precision required in handling battery cells, where both accuracy and consistency are critical.

From Fixed Automation to Embodied Systems

The pilot signals a broader shift in manufacturing automation.

Traditional automotive factories rely heavily on fixed robotic arms programmed for specific tasks. While efficient, these systems lack flexibility and require reconfiguration when production lines change.

Humanoid and mobile robots, by contrast, are designed to adapt to different tasks and environments. Their ability to navigate spaces and manipulate objects more dynamically could make them suitable for a wider range of applications within factories.

SAIC-GM’s approach combines mobility with humanoid-style manipulation, suggesting a hybrid path toward more flexible automation.

The company says it has tested the robots across more than 100 potential workstations before selecting battery assembly as the first deployment scenario.

Expanding the Role of Humanoid Robots

The current deployment is limited in scope, but SAIC-GM plans to expand the use of embodied robots across additional areas of production and logistics.

The company is also exploring bipedal humanoid robots alongside wheeled systems, indicating that different robot forms may coexist depending on the requirements of specific tasks.

The move comes as automakers globally are experimenting with humanoid robotics. Companies including Tesla and BMW have begun testing similar systems for manufacturing tasks, while Chinese manufacturers are accelerating development of embodied AI platforms.

For SAIC-GM, the integration of humanoid robots into a live production line suggests that the technology is beginning to move beyond demonstration and into early industrial application.

If the pilot proves successful, it could signal a gradual transition toward more adaptive, AI-driven automation in automotive manufacturing, where robots are no longer confined to fixed positions but operate as flexible workers within the production environment.

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Researchers at the Massachusetts Institute of Technology have developed a wearable ultrasound wristband that allows users to control robotic hands using their own natural movements, a breakthrough that could improve dexterity in both robotics and virtual reality systems.

The device captures detailed images of muscles and tendons in the wrist as a person moves their fingers and hand. Artificial intelligence software then translates those images into precise hand positions, enabling real-time control of robotic systems.

In demonstrations, users wearing the wristband were able to manipulate a robotic hand wirelessly, performing actions such as playing simple piano notes or shooting a miniature basketball into a hoop. The system also allowed users to control objects in a digital environment, pinching or rotating virtual items with natural gestures.

The research highlights a growing effort to bridge the gap between human motor control and robotic manipulation.

Seeing the Mechanics of the Human Hand

Replicating the dexterity of the human hand has long been one of the most difficult challenges in robotics.

A human hand contains dozens of muscles and joints working together to produce subtle and continuous movements. Capturing those movements accurately enough to control a robot has traditionally required complex camera setups or sensor-filled gloves.

The MIT team approached the problem differently by focusing on the wrist, where the muscles and tendons responsible for finger movement are located.

The wearable device incorporates a miniaturized ultrasound sensor similar to those used in medical imaging. Positioned on the wrist, the sensor continuously captures images of the internal structures that control the hand.

Each ultrasound image reflects the changing state of muscles and tendons as they contract and relax. Because these structures act like strings pulling the fingers, researchers realized the images could be used to infer the exact position of the hand.

AI Translates Motion into Robot Control

To interpret the ultrasound data, the team trained an artificial intelligence model to recognize patterns in the images and associate them with specific hand positions.

Human fingers have 22 degrees of freedom, meaning they can move in many subtle combinations. By analyzing different regions of the ultrasound images, the system can estimate how each finger is positioned and how the hand is oriented.

During training, volunteers performed various gestures while cameras recorded their hand movements. The researchers matched those recorded positions with the ultrasound images to create a dataset used to train the AI system.

Once trained, the algorithm could accurately predict hand movements directly from ultrasound data.

In testing, participants used the wristband to perform a range of gestures, including forming the letters of the American Sign Language alphabet and manipulating objects such as bottles, scissors and tennis balls.

Implications for Humanoid Robots

Beyond human-machine interfaces, the researchers believe the wristband could play a significant role in training future humanoid robots.

One challenge in robotics is collecting large datasets that capture how humans manipulate objects in real-world environments. By recording wrist ultrasound data from many people performing everyday tasks, researchers could build datasets that teach robots more natural manipulation skills.

Such data could eventually help robots learn delicate tasks such as assembling electronics, handling tools or even assisting with surgical procedures.

The technology could also replace existing hand-tracking methods used in virtual and augmented reality systems. Compared with camera-based systems, wearable ultrasound sensors are less affected by lighting conditions or visual obstructions.

The MIT team plans to further miniaturize the device and expand the training dataset with more users and gestures.

If successful, wearable ultrasound interfaces could provide a more intuitive way for humans to interact with machines – and potentially offer robots a better model for replicating the remarkable dexterity of the human hand.

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