A new robotics startup focused on industrial labor automation has raised $52 million to accelerate deployment of physical AI systems designed for some of the most demanding jobs in modern infrastructure.

Silicon Valley-based RoboForce announced the funding round led by YZi Labs, with additional backing from investors including technology entrepreneurs and institutional partners. The company is developing a full-stack robotics platform aimed at replacing or augmenting human labor in sectors such as renewable energy construction, logistics, mining, and data center development.

The investment signals growing investor interest in what many industry leaders describe as the next phase of artificial intelligence: machines capable of operating in the physical world rather than purely digital environments.

RoboForce’s flagship system, known as TITAN, is designed to work in environments where heat, repetition, and safety risks make human labor increasingly difficult to sustain. The company says it has already received letters of intent representing demand for more than 11,000 robots as it transitions from pilot deployments to larger-scale production.

Automation for the Hardest Industrial Jobs

The company’s founding thesis emerged from firsthand observation of labor-intensive industrial work.

Co-founder and CEO Leo Ma, who previously worked on autonomous systems and mobility technologies, has described visiting numerous industrial sites where the same challenge repeatedly appeared: physically demanding jobs that were difficult to staff consistently.

Solar energy construction offers a clear example. Utility-scale solar installations require workers to secure millions of panels across large outdoor sites, often in extreme heat. In the United States alone, labor shortages contributed to delays affecting tens of gigawatts of solar capacity in recent years.

Similar gaps exist across logistics hubs, mining operations, and infrastructure construction. These jobs require endurance, precision, and safety compliance, but often struggle to attract or retain workers.

RoboForce is positioning its robots as a solution to this structural workforce gap. TITAN is designed for millimeter-level precision and sustained operation in harsh environments, allowing it to perform tasks such as assembly, installation, and materials handling in large industrial projects.

Building a Physical AI Data Flywheel

Beyond the hardware itself, the company’s strategy centers on what it calls a “physical AI data flywheel”.

Each deployed robot collects operational data from real-world environments. That data feeds back into RoboForce’s foundation model, allowing the system to improve its capabilities over time and adapt to new industrial tasks.

The concept mirrors trends in autonomous vehicles and large-scale AI systems, where real-world data becomes a key competitive advantage. The more robots operating in the field, the faster the learning cycle accelerates.

RoboForce is developing its platform in collaboration with NVIDIA’s robotics ecosystem. Its systems use NVIDIA Jetson Thor for edge computing while relying on Isaac simulation tools, Isaac Lab training frameworks, and Cosmos world models to train robotic behaviors before deploying them in physical environments.

The approach allows robots to practice tasks in simulation and refine them with real-world feedback, narrowing the gap between experimental demonstrations and production deployment.

The company’s visibility increased when NVIDIA CEO Jensen Huang highlighted RoboForce’s technology during a keynote presentation at GTC, framing AI-powered robotics as a key driver of a broader industrial transformation.

Investors Bet on Physical AI Infrastructure

For YZi Labs, which manages more than $10 billion in assets, the investment reflects a growing conviction that robotics will become a central layer of future infrastructure.

Ella Zhang, managing partner and head of the firm, said the investment aligns with the belief that the next wave of AI innovation will extend beyond digital applications into machines that interact directly with the physical world.

Zhang will join RoboForce’s board as part of the investment.

The company was founded in 2023 by engineers and researchers from organizations including Carnegie Mellon University, the University of Michigan, Amazon Robotics, Google, Waymo, Tesla Robotics, and ABB.

The funding will be used to expand the company’s robot foundation models, scale manufacturing of its robotic systems, and convert existing pilot programs into full production deployments.

For the broader robotics sector, the deal reflects a wider shift in how automation is framed. Rather than focusing solely on factory efficiency, a growing number of companies are targeting labor-intensive sectors where workforce shortages threaten economic growth.

If those systems prove reliable at scale, robots may increasingly become a structural component of infrastructure development itself – helping build the energy systems, data centers, and logistics networks that underpin the global economy.

The race to bring artificial intelligence into the physical world is accelerating, and NVIDIA is positioning itself at the center of the emerging robotics stack.

At its recent announcements surrounding the GTC conference, the company unveiled a broader physical AI platform combining simulation software, world models, and robotics foundation models designed to support the development and deployment of intelligent machines. The initiative is backed by partnerships with major robotics companies including ABB Robotics, FANUC, KUKA, Agility Robotics, Figure, Universal Robots, and Yaskawa.

The effort reflects a wider shift across the robotics industry. As robots become more autonomous and adaptable, companies are moving beyond traditional automation toward systems that can perceive environments, reason about tasks, and act with greater flexibility.

NVIDIA founder and CEO Jensen Huang framed the shift as a structural change in industrial technology. “Physical AI has arrived,” Huang said, arguing that many industrial companies will increasingly operate as robotics companies as intelligent machines become embedded in manufacturing, logistics, infrastructure, and transportation systems.

Building the Infrastructure for Robot Intelligence

The company’s robotics strategy centers on providing the underlying computational and software infrastructure required to train and operate intelligent robots at scale.

New components include updated NVIDIA Isaac simulation frameworks, the Cosmos family of world models, and Isaac GR00T robot foundation models designed to help robots learn generalized skills across different environments. Together, these tools allow developers to generate synthetic environments, train policies in simulation, and transfer those behaviors to real machines.

Simulation plays a central role. Industrial robotics companies including ABB, FANUC, Yaskawa, and KUKA are integrating NVIDIA’s Omniverse and Isaac technologies to create digital twins of production lines, allowing engineers to design and test robotic systems virtually before deploying them on factory floors.

The companies are also incorporating NVIDIA Jetson edge computing modules into their controllers to enable real-time AI inference directly on robots. With millions of industrial robots already operating globally, these integrations aim to gradually layer advanced intelligence onto existing automation infrastructure.

The approach reflects a broader industry consensus that robotics development will increasingly rely on large-scale simulation, synthetic data generation, and foundation models rather than traditional rule-based programming.

A Push Toward General Purpose Robot Brains

Another key focus of the initiative is the development of generalized robotic intelligence.

Companies such as Skild AI and FieldAI are using NVIDIA’s Cosmos world models and Isaac simulation environments to train AI systems that can operate across different robotic embodiments. Instead of building task-specific software for every application, developers are attempting to create “robot brains” capable of adapting to new environments and tasks with limited retraining.

One of the most visible deployment efforts involves Skild AI working with ABB Robotics and Universal Robots to integrate generalized AI systems into widely deployed industrial and collaborative robots. The goal is to expand automation into more dynamic tasks that traditionally required human adaptability.

Skild AI is also collaborating with Foxconn on assembly systems used in NVIDIA’s Blackwell chip production lines. These systems rely on AI-driven dual-arm manipulators designed to perform highly precise electronics assembly operations.

The broader strategy aligns with NVIDIA’s belief that the next generation of robots will combine the reliability of industrial automation with the adaptability of modern AI systems.

Humanoid Robots and Surgical Systems Join the Platform

Beyond industrial automation, NVIDIA’s ecosystem now extends into humanoid robotics and healthcare.

Developers including Agility Robotics, Figure, NEURA Robotics, and AGIBOT are using the company’s simulation tools and robotics models to accelerate development of humanoid robots capable of operating in human environments. Building such machines requires integrating perception, locomotion, dexterous manipulation, and decision-making within tightly constrained safety requirements.

Healthcare robotics is another area of expansion. Companies including CMR Surgical, Johnson & Johnson MedTech, and Medtronic are using NVIDIA simulation and computing platforms to train and validate AI-assisted surgical systems before clinical deployment.

These applications require particularly strict validation processes, making simulation and digital twin technology especially valuable.

The expansion of NVIDIA’s robotics ecosystem comes as demand for AI computing continues to surge. Huang recently projected that AI chip sales could eventually reach $1 trillion annually as industries transition toward what he described as a new computing era driven by AI systems embedded across both digital and physical infrastructure.

For robotics, the implication is clear: as machines become more capable of perceiving and interacting with the real world, the boundary between AI software and industrial hardware is increasingly dissolving. Companies that control the infrastructure connecting those layers may shape how quickly intelligent machines move from research labs into everyday operations.

Samsung Electronics is placing a major strategic bet on one of the most difficult problems in robotics: building robot hands capable of manipulating objects with human-like precision.

The company recently established a new research group called Hand Lab within its Future Robotics Task Force. The initiative focuses on developing advanced robotic hands that could eventually enable humanoid robots and automated manufacturing systems to handle delicate tasks currently performed by humans.

Industry analysts view the move as a signal that Samsung intends to compete more aggressively in the emerging humanoid robotics market.

While robots have become increasingly capable of walking, navigating environments, and maintaining balance, engineers say the real challenge lies elsewhere – dexterous manipulation.

Why Robotic Hands Matter

In robotics research, the ability to move like a human is no longer the primary obstacle.

Modern robots can climb stairs, recover from falls, and navigate complex environments with increasing reliability. But performing tasks that humans consider simple – tightening a screw, picking up a fragile object, or assembling small components – remains extremely difficult.

These tasks require a combination of force control, tactile feedback, and coordinated finger motion that traditional industrial robots struggle to achieve.

Most factory robots rely on simple grippers designed for highly structured environments. Humanoid robots, however, must interact with tools, components, and devices originally designed for human hands.

The result is a growing consensus within robotics research that the future of humanoid robots depends heavily on hand design.

Samsung’s decision to create a specialized research group dedicated to robotic hands reflects this shift in priorities.

A Tendon-Driven Approach to Dexterity

According to industry reports, Samsung’s robotic hand project is exploring a tendon-driven design, a system inspired by the anatomy of the human hand.

Instead of placing motors directly inside each finger, artificial tendons – cables running through the arm – pull and control finger movements. This architecture allows for smoother motion, finer force control, and potentially greater energy efficiency.

The approach is significantly more complex to engineer and manufacture than conventional robotic grippers, which is why most industrial robots avoid it.

However, tendon-driven systems can produce more natural and adaptable movements, making them well suited for humanoid robotics.

Samsung also plans to incorporate tactile sensors that allow robotic fingers to detect pressure, texture, and contact forces. These signals could feed into machine-learning systems that help robots adjust their grip in real time.

Such capabilities are considered essential for what researchers increasingly call physical AI – systems that combine artificial intelligence with real-world robotic interaction.

Building a Robotics Ecosystem

Samsung’s focus on robotic manipulation is part of a broader strategy to build a vertically integrated robotics ecosystem.

Over the past several years, the company has expanded its investments in robotics technology across multiple business units.

Samsung SDI is developing batteries tailored for robotics systems, while Samsung Electro-Mechanics is working on actuators and components for robotic motion.

The company also acquired a controlling stake in Korean robotics developer Rainbow Robotics, known for its humanoid and dual-arm robotic platforms.

Together, these initiatives could allow Samsung to integrate hardware, sensors, computing, and AI into a unified robotics platform.

The company has also outlined a longer-term plan to create AI-powered autonomous factories by 2030, where intelligent robots perform tasks ranging from logistics and inspection to complex assembly.

In such environments, robotic hands capable of delicate manipulation could become the key enabling technology.

Global Competition Intensifies

Samsung’s push into robotic manipulation also reflects rising global competition in humanoid robotics.

China’s robotics sector is expanding rapidly, with analysts projecting tens of thousands of humanoid robots could be produced annually within the next few years.

Chinese manufacturers have already achieved scale in service robots such as delivery and cleaning machines, often competing on cost.

Samsung appears to be taking a different approach – focusing on technological differentiation rather than mass production.

If the company succeeds in developing robotic hands capable of human-level dexterity, it could unlock new applications not only in electronics manufacturing but also across logistics, construction, and industrial automation.

Within robotics circles, engineers often summarize the challenge with a simple observation:

Many robots can walk.

Very few can truly use their hands.

Samsung’s new Hand Lab is designed to change that.