Robots have made rapid progress in vision and motion, but touch has remained a persistent limitation. Without reliable tactile feedback, even advanced systems struggle to handle fragile objects or safely interact with humans. A new class of flexible sensors developed by researchers at Penn State suggests that gap may be narrowing.

The team has created a lightweight “robotic skin” capable of detecting extremely small pressure changes while maintaining durability under repeated use. The development reflects a broader push in robotics to move beyond perception and mobility toward physical intelligence – systems that can interpret and respond to the physical world with greater nuance.

Turning Pressure into Real Time Control

At the core of the system is a small, flexible sensor built around graphene aerogel, a porous material that converts mechanical pressure into electrical signals. The structure allows the sensor to respond quickly to light touch while remaining stable under heavier loads, addressing a common tradeoff between sensitivity and durability.

Each sensor can register contact in just over 100 milliseconds and recover shortly after, enabling near real-time feedback. When arranged in arrays, these sensors generate pressure maps that function similarly to human skin, allowing robots to interpret how force is distributed across their surface.

This capability shifts tactile sensing from passive measurement to active control. In demonstrations, robotic hands equipped with the sensors adjusted grip strength dynamically, preventing damage to delicate objects such as soft food items. The system effectively translates touch into immediate motor responses, closing a loop that has historically been difficult to achieve in robotics.

From Grasping to Perception

Beyond simple force control, the sensor system introduces a new layer of perception. By analyzing pressure patterns, robots can begin to distinguish between different materials and objects based on how they respond to touch.

In experimental tests, researchers trained a lightweight model to classify food items using tactile data alone. After repeated training cycles, the system achieved accuracy above 99%, suggesting that touch-based recognition could complement or, in some cases, substitute for visual input.

This has implications for environments where vision is unreliable, such as cluttered industrial settings or domestic spaces with variable lighting. It also aligns with a growing interest in multimodal AI systems that combine vision, language, and physical interaction.

The same sensing approach has also been applied to wearable devices, where it can track pulse signals and joint movement with consistent accuracy. This points to potential crossover applications in healthcare, prosthetics, and rehabilitation.

Expanding the Role of Tactile Intelligence

The development highlights a broader shift in robotics toward integrating sensing, control, and learning into unified systems. While vision-based AI has dominated recent advances, tactile intelligence is emerging as a critical component for real-world deployment.

Companies such as Tesla and Nvidia have emphasized the importance of physical interaction in next-generation AI systems, particularly in humanoid robotics and automation. However, progress in touch sensing has lagged behind advances in perception and planning.

The Penn State research suggests that scalable, low-cost tactile systems may begin to close that gap. The sensors can also detect pressure changes in non-robotic contexts, such as monitoring swelling in battery systems – an early indicator of potential failure in electric vehicles.

Despite the progress, the technology remains in an early stage. Challenges include miniaturization, long-term reliability, and integration with existing robotic platforms. Researchers are also exploring ways to expand the sensing capabilities to include temperature and stretch, bringing the system closer to the complexity of human skin.

The ability to sense and respond to gentle touch is likely to be a defining feature of next-generation robots, particularly as they move into homes, healthcare settings, and collaborative workplaces. While the current system is still experimental, it illustrates how advances in materials science and AI are converging to address one of robotics’ most persistent limitations.

If scaled successfully, tactile sensing could shift robots from rigid, pre-programmed machines to adaptive systems capable of interacting with the physical world in a more human-like way.

Artificial Intelligence (AI), News, Robots & Robotics, Science & Tech

The idea of robots operating inside the human body has long been associated with science fiction. But recent advances in DNA-based nanotechnology are beginning to translate that vision into early-stage experimental systems, where programmable molecular machines can move, sense, and interact with biological environments.

Researchers are now designing DNA “robots” capable of delivering drugs directly to diseased cells and identifying viral threats within the bloodstream. While these systems remain far from clinical deployment, they represent a shift in how robotics is defined – extending from mechanical systems into the molecular domain.

Reimagining Robotics at the Molecular Scale

Unlike conventional robots built from metal, electronics, and actuators, DNA robots are constructed from strands of nucleic acids that can be folded, connected, and programmed into functional structures. Using techniques inspired by origami, scientists can create rigid joints, flexible linkages, and dynamic components that mimic mechanical systems at a nanoscale.

This approach adapts established principles from traditional robotics – including rigid-body motion and compliant mechanisms – into a biochemical context. The result is a new class of machines that operate not through motors or gears, but through chemical interactions and structural transformations.

Controlling these systems presents a fundamental challenge. At the molecular level, motion is dominated by random thermal fluctuations, known as Brownian motion, which can disrupt precise behavior. To address this, researchers rely on biochemical programming methods such as DNA strand displacement, where specific sequences act as triggers to initiate movement or change configuration.

External signals, including light, magnetic fields, and electric fields, can also be used to guide these nanorobots, providing an additional layer of control in otherwise unpredictable environments.

Medical Applications Remain Experimental

The most immediate interest in DNA robotics lies in medicine, where the ability to operate at cellular or even molecular resolution could enable highly targeted interventions. In experimental settings, DNA robots have been designed to locate specific cell types, release therapeutic payloads, and potentially capture or neutralize viruses.

Such systems could function as “nano-surgeons”, delivering drugs with far greater precision than conventional treatments and reducing side effects associated with systemic therapies. Researchers are also exploring their potential to detect and bind to viral particles, including pathogens similar to COVID-19, as a step toward autonomous diagnostic or therapeutic platforms.

Beyond medicine, DNA robots may also serve as tools for nanoscale manufacturing. By positioning molecules and nanoparticles with sub-nanometer precision, they could enable new forms of computing and materials engineering that are difficult to achieve with existing fabrication techniques.

However, most current systems remain proof-of-concept demonstrations. They typically operate in controlled laboratory conditions and lack the robustness required for real-world biological environments.

From Proof of Concept to Scalable Systems

The transition from experimental prototypes to practical applications presents several challenges. In addition to environmental unpredictability, researchers face limitations in modeling and design. There is currently no comprehensive database of DNA mechanical properties, and simulation tools for predicting nanorobot behavior remain underdeveloped.

Scaling these systems will likely require advances across multiple domains, including bio-manufacturing, materials science, and artificial intelligence. Proposed approaches include the development of standardized DNA component libraries and the use of AI-driven design tools to optimize structures and predict performance.

The broader implication is that robotics may increasingly extend beyond traditional hardware into programmable biological systems. DNA robots, if successfully scaled, could redefine automation at the smallest possible level – enabling machines that operate not in factories or warehouses, but within cells and molecules themselves.

For now, the technology remains in its formative stage. But its trajectory suggests that the next phase of robotics innovation may be less about building larger, more capable machines, and more about engineering systems that can function where conventional robots cannot reach.

News, Robots & Robotics, Science & Tech

LG CNS is deepening its push into physical AI through a set of partnerships with Silicon Valley robotics startups, signaling a shift from enterprise software toward integrated AI and robotics systems. The move reflects a broader trend among large technology firms seeking to secure both the software intelligence and hardware platforms required for real-world automation.

The South Korean company announced that it has partnered with U.S.-based startups Config and Dexmate following its Open Innovation Summit held in Silicon Valley on March 19. The initiative is part of an ongoing effort to identify early-stage technologies that can be incorporated into LG CNS’s enterprise-focused AI and automation offerings.

Combining Robot Foundation Models with Hardware

At the center of the partnerships is Config, a startup focused on robot foundation models, a category of AI systems designed to generalize across tasks in physical environments. The company’s technology enables robots to learn from human motion data, translating real-world demonstrations into structured training inputs for robotic systems.

LG CNS plans to integrate Config’s models into its robotics stack to improve precision in dual-arm manipulation, a capability widely seen as critical for industrial automation and service robotics. Unlike traditional robotic programming, which relies on predefined instructions, these models aim to allow robots to adapt to variable environments with less manual configuration.

The partnership with Dexmate, meanwhile, extends LG CNS’s reach into hardware. Dexmate develops humanoid robots equipped with dual arms and wheel-based mobility, offering an alternative to bipedal locomotion that can simplify stability and deployment in structured environments.

LG CNS had previously invested in Dexmate, and the expanded partnership suggests a longer-term strategy of aligning software capabilities with specific hardware platforms rather than remaining hardware-agnostic.

Expanding the Definition of Physical AI

The company’s approach reflects an evolving definition of physical AI, where progress depends on the interaction between machine learning models and mechanical systems rather than advances in either domain alone. By working with both a model developer and a hardware manufacturer, LG CNS is positioning itself within a growing ecosystem that spans perception, control, and actuation.

This mirrors broader industry developments led by companies such as Nvidia, which has promoted integrated frameworks combining simulation, AI training, and robotics deployment. The emphasis on full-stack systems is becoming increasingly important as robotics moves from controlled demonstrations to operational environments.

LG CNS’s expansion into wheel-based humanoid systems also suggests a pragmatic approach to deployment. While bipedal robots remain a long-term goal for many developers, hybrid designs that prioritize stability and efficiency are gaining traction in logistics, manufacturing, and service applications.

Open Innovation as a Scaling Strategy

The partnerships were announced as part of LG CNS’s broader open innovation program, which seeks to identify and collaborate with startups at an early stage. This model allows large enterprises to access emerging technologies without building all capabilities in-house, while giving startups a pathway to commercial deployment.

For LG CNS, the strategy appears aimed at accelerating its transition from enterprise IT services into a provider of AI-driven automation infrastructure. By combining internal capabilities with external innovation, the company is attempting to build a flexible ecosystem that can adapt as both AI models and robotics hardware continue to evolve.

The challenge, as with much of the physical AI sector, lies in translating technical capability into scalable, real-world use cases. While partnerships can accelerate development, widespread deployment will depend on whether these integrated systems can deliver consistent performance in complex environments.

Artificial Intelligence (AI), Business & Markets, News, Startups & Venture

The planned public listing of Unitree Robotics marks a turning point for the humanoid robotics sector, which has long been defined by prototypes, research funding, and speculative timelines. By moving toward an initial public offering, the Hangzhou-based company is positioning itself as one of the first large-scale tests of whether humanoid robots can sustain a viable commercial market.

Unitree filed to list on the Shanghai Stock Exchange on March 20, seeking to raise 4.2 billion yuan, or about $610 million, to expand manufacturing and research. The company’s trajectory, from viral demonstrations to profitability within a year, places it at the center of a broader shift in how robotics companies are financed and evaluated.

Profitability Arrives Ahead of Mass Adoption

Unlike many peers, Unitree enters the public markets with profitability already established. The company reported an adjusted net profit of 600 million yuan in 2025, a sharp increase from its first profitable year in 2024. Revenue rose to 1.71 billion yuan from 392 million yuan the previous year, reflecting both volume growth and expanding product adoption.

This distinguishes Unitree from earlier entrants such as UBTech Robotics, which has remained unprofitable despite going public. The contrast highlights a widening gap between companies still operating in development mode and those beginning to scale production.

Even so, the market remains early. More than 100 humanoid robotics companies currently operate in China, according to Counterpoint Research, with consolidation expected as capital markets begin to impose stricter performance expectations. Unitree’s IPO is likely to serve as an early signal of which business models can sustain investor confidence.

From Quadrupeds to Humanoids

Unitree’s growth has been driven in part by a transition from quadruped robots to humanoid systems. The company shipped more than 30,000 quadrupeds between 2022 and 2025, establishing a hardware and supply chain base before scaling humanoid production.

In 2025, it sold 5,500 humanoid robots, which accounted for over half of its core revenue, up from less than 2% two years earlier. The majority of these units were sold to research institutions and educational users, indicating that widespread enterprise deployment remains limited.

The shift reflects a broader industry pattern, in which quadruped platforms have served as an intermediate step toward more complex humanoid systems. These earlier products provide revenue, operational data, and manufacturing experience that can be transferred into humanoid development.

Falling Prices and Vertical Integration

One of the more notable signals in Unitree’s prospectus is the rapid decline in pricing. The average price of its humanoid robots fell from roughly 593,400 yuan in 2023 to 167,600 yuan in 2025, bringing systems closer to a range that could support broader adoption.

At the same time, gross margins improved to nearly 60%, suggesting that cost reductions are being driven by manufacturing efficiencies rather than discounting alone. Unitree attributes this to its strategy of developing and producing key components in-house, reducing reliance on external suppliers.

This combination of falling prices and improving margins remains rare in the humanoid robotics sector, where most companies are still managing high costs and limited production volumes.

However, external dependencies remain. Like many robotics developers, Unitree relies on computing platforms and chips from Nvidia for core processing capabilities, leaving part of its supply chain exposed to geopolitical and trade uncertainties.

A Market Signal for Physical AI

Unitree’s IPO arrives amid intensifying global competition in humanoid robotics. In the United States, Elon Musk has said that Tesla plans to begin retail sales of its Optimus robots by 2027, framing humanoids as a future mass-market product.

At the same time, the concept of “physical AI” – systems that combine machine learning with real-world interaction – is gaining traction across the industry. Unitree’s robots were featured alongside other platforms at a recent conference led by Jensen Huang, underscoring growing alignment between hardware manufacturers and AI infrastructure providers.

Despite this momentum, near-term demand remains concentrated in research, education, and controlled industrial environments. Unitree’s own projections, which include plans to produce tens of thousands of humanoids annually within five years, suggest confidence in scaling, but not necessarily immediate mass adoption.

The company’s public listing will therefore function as more than a financing event. It will offer one of the first measurable indicators of whether investors view humanoid robotics as an emerging industrial category or as a longer-term technological bet.

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