What happens when you give AI coding agents a lab full of robotic arms, some compute resources, and a “generous token budget” for teaching the robots various tasks? The agents can apparently figure out a training regimen that teaches the robots to successfully cut zip ties and even insert GPUs into thin sockets on motherboards.

That glimpse into how AI can act in a fully autonomous way to automate robot training was made possible by a new agent harness framework—software that wraps around AI models to enable their use of various tools while also providing capabilities such as memory, context, constraint, and feedback loops. That agentic harness, called ENPIRE, was developed by robotics researchers at the Nvidia GEAR (Generalist Embodied Agent Research) lab alongside collaborators from Carnegie Mellon University in Pittsburgh and the University of California, Berkeley.

“A part of our NVIDIA GEAR lab now self-improves tirelessly overnight,” wrote Jim Fan, director of AI at NVIDIA, in a LinkedIn post. “We just read the reports in the morning.”

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A plain-English guide to AI in 2026, including top chatbots, free AI tools, humanoid robots, key terms, and what comes next.

A groundbreaking advancement in nanotechnology has emerged from the University of Basel, Switzerland, where a research team has engineered a sophisticated modular nanorobot exhibiting unparalleled versatility. This innovative system comprises two distinct, reusable components: a propulsion module and a payload capsule, which autonomously self-assemble through a DNA-based molecular Velcro mechanism. Their dynamic interplay offers promising applications across medicine, industry, and environmental science, heralding a transformative direction in nanorobotics research.

Nanorobotics has often been confined to the realm of science fiction, evoking images of minuscule machines navigating within the human body or cleaning up environmental pollutants. However, the rapid progress in this field reflects a new reality where these tiny constructs—differing fundamentally from conventional robots due to their biochemical and nanoscale nature—have started to fulfill tangible roles. Unlike traditional robots equipped with electronic circuits and software, these nanorobots utilize biomolecules and nanoparticles as their building blocks, innovatively combining bioengineering and materials science.

The design philosophy underlying the Basel team’s nanorobot addresses a critical limitation that has hampered previous efforts: specificity of function. Traditional nanorobots tend to be single-purpose entities, finely tuned for a particular task and lacking adaptability. In contrast, this modular system’s architecture allows for the independent development and exchange of functional units, rendering it highly customizable. Such adaptability is pivotal for extending the utility of nanorobots across diverse scenarios, from executing targeted drug delivery within the human body to catalyzing chemical reactions in industrial environments.

At the core of the nanorobot’s structure lies two integral modules. The first is a magnetic propulsion unit, which empowers the nanorobot with directed mobility under external magnetic fields. This enables precise navigation through complex environments—a critical capability for any targeted therapeutic or catalytic application. The second module serves as a payload capsule, designed to securely house and transport therapeutic agents, enzymes, or other functional molecules to desired locations. This segregation of propulsion and payload functions epitomizes engineering elegance at the nanoscale.

The payload capsule is particularly remarkable due to its internal composition. It encapsulates four enzyme-loaded polymer vesicles—nanoscale spherical structures synthesized from polymers that can safeguard and regulate the biochemical cargo within. These vesicles are equipped with nanoscaled pores, facilitating the ingress of substrate molecules and the egress of reaction products while protecting the encapsulated enzymes from premature degradation or deactivation. Moreover, depending on the specific configuration, these vesicles can be selectively triggered to release bioactive substances on demand, a feature critical for controlled therapeutic interventions.

Achieving a robust yet reversible connection between the propulsion module and payload capsule, the researchers employed a DNA-based “Velcro” assembly system. This approach utilizes complementary strands of DNA grafted on the surfaces of both modules, fostering specific and programmable binding through Watson-Crick base pairing. Such molecular precision guarantees that the modules self-assemble and remain stably coupled during operation, yet can be separated and recombined efficiently. This dynamic assembly-disassembly paradigm allows for easy reloading of the payload and recycling of the propulsion units, a major advantage for scalability and cost-efficiency.

Another notable feature enhancing the nanorobot’s functionality is the strategic modification of the payload capsule with biomolecular ligands that enable targeted docking onto specific cells or substrates. Using the human cancer cell line HeLa as a test model, the scientists demonstrated that these nanorobots, when loaded with fluorescent markers, preferentially accumulate on the cell surfaces. This active targeting capability is instrumental for precise delivery of therapeutic agents, ensuring minimal off-target effects and enhancing treatment efficacy.

In a striking demonstration of biomedical potential, the enzyme-laden nanorobots successfully synthesized an anticancer drug directly at the targeted site, reducing the viability of HeLa cells to a mere 16 percent within 72 hours. This localized drug production exemplifies an advanced therapeutic approach where site-specific synthesis and delivery converge, potentially overcoming challenges related to systemic side effects and suboptimal drug concentrations. According to Dr. Voichita Mihali, the lead author, this concentration of drug activity represents a promising strategy for future oncological applications.

Beyond the biomedical sphere, the modular nanorobot’s catalytic prowess, combined with its magnetic propulsion, opens compelling avenues in environmental and industrial contexts. For instance, nanoscale catalysts capable of performing complex chemical transformations can be transported magnetically to reaction sites, where they perform precise functions before being retrieved for reuse. This magnetically enabled recyclability addresses a critical sustainability challenge, aligning with growing demands for environmentally responsible and economically viable technologies.

The ability to dissociate the two modules after task completion and reload the payload capsules before reassembling them underscores the potential for repeated usability. This reusability is vital for practical implementations where cost limitations and material efficiency are paramount. Moreover, it facilitates adaptability, as different payloads can be swapped in according to the specific functional requirement, whether it be therapeutic commands or industrial chemical catalysis.

Importantly, the modular design philosophy extends the nanorobot’s versatility beyond its current framework. By altering the composition of the payload capsule, this platform could be customized for a plethora of applications without necessitating complete redesign. This adaptability accelerates translational research by leveraging a common technological foundation to address multiple challenges spanning healthcare, environmental remediation, and manufacturing.

The interdisciplinary nature of this project also highlights effective scientific collaboration, combining expertise from the National Center of Competence in Research – Molecular Systems Engineering, the Swiss Nanoscience Institute, and Heidelberg University. Such collaborative efforts are critical to tackling the complex engineering and biological challenges inherent in creating functional nanomachines capable of interacting reliably within diverse and dynamic environments.

While the modular nanorobot represents a significant leap forward, its ultimate deployment in human clinical settings remains a long-term objective, contingent on extensive biocompatibility and safety validation. Nonetheless, its current design and demonstrated functionalities establish a crucial foundation. The system’s adaptability and multifunctionality promise to catalyze new directions in nanomedicine and industrial nanotechnology, potentially revolutionizing how minute robots operate and impact their environments.

In conclusion, the University of Basel’s innovative modular nanorobot not only redefines the capabilities of nanoscale machines but also introduces an engineering paradigm centered on modularity, programmability, and reusability. Its ability to combine magnetic propulsion with a versatile, enzyme-loaded payload paves the way for localized therapies and catalysis with unprecedented precision and efficiency. This research marks a transformative milestone that will likely inspire continued exploration into modular nanorobotics, nurturing advancements that could one day address some of the most pressing challenges in medicine and technology.

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Subject of Research: Modular Nanorobots with Enzyme-Based Catalytic Activity and Magnetic Propulsion

Article Title: Multiplex Modular Nanorobotic Systems with Catalytic Activity under Magnetic Navigation

Web References: https://doi.org/10.1002/adfm.202600079

References: Advanced Functional Materials, University of Basel Research Articles

Image Credits: Voichita Mihali, University of Basel

Keywords

Nanorobots, Modular Nanorobotics, Enzyme Catalysis, Magnetic Propulsion, Targeted Drug Delivery, Polymer Vesicles, DNA-Based Assembly, Cancer Therapy, Biocompatible Nanomachines, Reusability, Molecular Velcro, Nanomedicine

Hong Kong plans a 24-hour convenience store run by Galbot’s Xiao Gai humanoid robot, bringing embodied AI into public retail and customer service.

Stringman uses ceiling cables, cameras, and a robotic gripper to collect clutter, showing a lower-cost path for practical home robotics.

From facial recognition on smartphones to humanoid robots, computer vision technology, which serves as the eyes of artificial intelligence (AI), is widely used in daily life. A joint research team from KAIST and international institutions has developed a technology that allows AI to see the world more clearly with minimal memory, increasing GPU (Graphics Processing Unit) memory efficiency by up to 16 times. The achievement is seen as a core technology that could accelerate the era of humanoid robots and on-device AI.

Chinese firm Alibaba has launched its first embodied AI model family, which links large language models with real-world robotic actions.

The Qwen-Robot suite was developed by Alibaba’s Tongyi Lab and is undergoing pilot testing with selected Alibaba Cloud enterprise clients.

The suite comprises three models focused on navigation, manipulation, and world modeling for robots operating in physical environments.

Alibaba said the models enable machines to perceive, reason, and interact with the real world, joining a growing global push to advance embodied AI beyond traditional chatbot applications.

Robots meet reasoning

Alibaba says its Qwen family of AI models has become very good at understanding the physical world. These models can recognize objects, understand spatial relationships, follow complex visual instructions, and reason about real-world environments. For example, a model can understand a command such as, “Go to the kitchen, find the red cup, pick it up, and place it on the shelf.”

However, understanding a task is different from actually performing it. While a vision-language model (VLM) can describe the steps needed to complete a task, it cannot directly control a robot’s movements. The challenge is connecting human language and visual understanding with the motor actions required to interact with the physical world.

This problem is difficult because robot training data is very different from internet data. Information collected from navigation systems, robotic arms, vehicles, and cameras comes in different formats and is expensive to gather. Simply combining all this data often creates conflicts rather than improving performance.

To address this, Alibaba developed the Qwen-Robot Suite, which includes three specialized models. Qwen-RobotNav focuses on movement and navigation. It helps robots follow instructions, navigate to locations, track targets, and support autonomous driving.

According to its website, Qwen-RobotManip focuses on physical interaction. It enables robots to grasp, move, and manipulate objects using a large training dataset collected from different robotic systems. Qwen-RobotWorld acts as a world model, predicting how environments may change and helping robots understand the likely outcomes of their actions.

Together, these models aim to enable robots to understand instructions, interact with objects, navigate environments, and make decisions in the real world.

Physical AI accelerates

Alibaba showcased Qwen-RobotNav on a Unitree Go2 quadruped powered by NVIDIA Jetson Thor hardware and a single low-resolution camera. The robot successfully navigated an unfamiliar apartment, following spoken instructions across multiple rooms without preloaded maps, while maintaining an inference latency of 196 milliseconds.

The company claims that Qwen-RobotManip, its robotic manipulation model, was trained on more than 38,000 hours of open-source data covering object handling and interaction tasks. According to Alibaba, the model recently achieved the highest score in the generalist category of the RoboChallenge real-world robotics benchmark, earning a process score of 59.83 and a task success rate of 45 percent.

The company also unveiled Qwen-RobotClaw, a robotics agent framework that enables Qwen models to use the Qwen-Robot suite as physical-world tools. In one demonstration, an agent searched for a restroom, identified an out-of-order sign, and independently rerouted to another location. Alibaba further open-sourced Chat2Robot, a browser-based platform for testing embodied AI interactions.

As competition in embodied AI intensifies worldwide, Alibaba has expanded its ambitions beyond language and multimodal software with the launch of its Qwen-Robot models. The move reflects a broader industry shift toward creating AI systems capable of understanding and interacting with the physical world.

Alibaba’s move comes as competition in physical AI accelerates globally. In the US, Google DeepMind is advancing Gemini Robotics, while Nvidia is expanding its robotics ecosystem through Cosmos, Isaac, and GR00T. Start-ups, including Physical Intelligence, Skild AI, and Figure AI, are also developing general-purpose robotic intelligence, according to the South China Morning Post.

China is strengthening its position by pairing its manufacturing advantages with growing investments in AI software for autonomous decision-making. The sector now spans AI developers, robotics firms, and EV makers. Companies such as Alibaba, Tencent, Unitree, AgiBot, UBTech, Galbot, Spirit AI, GigaAI, Xpeng, and Xiaomi are actively pursuing embodied AI technologies.

An elephant’s trunk can haul a felled log across a clearing and, moments later, peel a single banana without bruising the fruit. That range — from raw strength to delicate precision — comes from a boneless, all-muscle structure that has fascinated soft robotics researchers for years.

A new study has now mapped exactly why the trunk’s skin enables that range, down to its microscopic architecture.

Researcher Lucia Beccai and colleagues studied trunk tissue obtained from a single adult Asian elephant (Elephas maximus) that died of natural causes at Zurich Zoo, Switzerland, in 2020.

Using 35 tissue samples taken across the specimen, the team combined biomechanical testing, histology, multiple imaging techniques, and computational modeling to characterize the trunk’s skin in unprecedented detail.

A skin split into two functional zones

The central finding is that the trunk’s skin is not a uniform material — it is sharply divided into two distinct mechanical zones, each engineered for an entirely different job.

The upper skin functions like an armor-like shield: tough, protective, and 3.14 times stiffer than the skin on the underside of the trunk. That stiffness makes sense functionally — the top of the trunk is what drags across rough bark, scrapes against the ground, and absorbs impact during heavy lifting and pushing.

The underside skin is the opposite: highly flexible and acutely sensitive, built specifically for grasping. Its low stiffness allows it to conform tightly to the contours of whatever object the elephant is handling — a log, a piece of fruit, another elephant’s trunk in greeting — maximizing contact area between the skin and the object and producing a steady, secure grip without requiring excessive muscular force.

A mechanical lens hidden beneath the surface

The most striking discovery lies just beneath the surface of the underside skin: dome-shaped structures called dermal papillae. Using finite element modeling — a computational technique that simulates how physical structures respond to stress — the researchers found that these papillae function like a subsurface mechanical lens.

Rather than simply cushioning the skin, the dome-shaped papillae concentrate and amplify mechanical stress at precisely the locations where the trunk’s sensory nerves are positioned.

In effect, the structure acts as a biological signal booster — taking a faint mechanical input, such as the texture of a banana peel, and intensifying it before it reaches the nerve endings responsible for interpreting touch.

What it means for robotics

According to the study’s authors, this two-zone, mechanically tuned architecture offers a direct, ready-made blueprint for engineers designing the next generation of soft robotic systems.

The trunk does not need separate “armor” and “sensor” components bolted together — it achieves both functions through a single continuous tissue with engineered variation in stiffness and subsurface geometry.

The practical implication: by using biomimetic multi-material approaches — fabricating robotic grippers from layered materials with deliberately varied stiffness, much like the trunk’s natural upper-lower dichotomy — engineers could build grippers with passive outer protection that simultaneously amplify faint tactile signals, all without exposing delicate sensors directly to mechanical wear and abrasion.

That combination has been a persistent challenge in soft robotic gripper design: sensors sensitive enough to detect subtle touch are often the same components most vulnerable to physical damage from repeated contact.

The elephant trunk’s solution — concentrating sensitivity beneath a protective, graduated-stiffness shield — offers a structural answer that robotics researchers have been seeking for years.

The study was published in the journal PNAS Nexus.

Many AI robots appear intelligent as long as they operate in controlled environments. Neuroscientist and robotics researcher Elisa Donati explains why robots with real-world readiness require more than just fast software.

An auto factory worker can remember the storage bin where she left a partly assembled component the night before and quickly return to that spot to pick it up. But robots that may work side by side with her would struggle to develop and access this same type of "spatiotemporal" memory.

Chinese automaker Seres has unveiled its first humanoid robot, Xiaosai, marking the company’s entry into the rapidly growing embodied AI sector. The robot is designed with visual recognition, autonomous greeting, and voice interaction capabilities.

The launch highlights the automaker’s expanding focus on robotics, with several additional embodied intelligence robots expected to debut later this year as Seres broadens its presence beyond electric vehicles.

Recently, Hyundai announced plans to deploy over 25,000 Atlas humanoid robots in the US, targeting 30,000 annual units by 2028 with locally manufactured robot components.

Industrial humanoid debut

Chinese automakers are rapidly expanding into humanoid robotics, leveraging expertise in manufacturing, artificial intelligence, supply chains, and automation to develop a new generation of embodied intelligence systems.

The latest entrant is Seres, which has unveiled its first humanoid robot, Xiaosai. The announcement was made by Seres Group director and vice president Kang Bo, who released a video showcasing the robot and confirmed that several additional embodied intelligence products are currently under development and scheduled to make their official debut later this year, reports CNEVPost.

Thought more details are not available, the humanoid is reported to be equipped with visual recognition capabilities, can autonomously greet people, and supports voice-based interaction. Its introduction highlights Seres’ broader efforts to integrate advanced robotics into manufacturing operations.

The company’s factory already employs a range of specialized robots across AI-powered quality inspection, logistics delivery, and production-line collaboration. Among them are Xiaosai 01, which performs quality inspections for chassis assembly, and Xiaosai 02, which checks the exterior configuration of completed vehicles.

According to Seres, the facility’s smart hub coordinates more than 1,600 intelligent devices, while over 3,000 industrial robots operate in synchronization throughout the plant. The factory also incorporates technologies, including the Internet of Things (IoT), big data, digital twins, 5G connectivity, and artificial intelligence, creating a highly automated digital ecosystem, reports CNEVPost.

Automakers go the robot way

Seres’ move into humanoid robotics builds on earlier investments in embodied intelligence. In October 2024, the company signed a cooperation agreement with Volcengine, a subsidiary of ByteDance, in Beijing. The partnership focuses on intelligent robot technologies, including decision-making systems, control technologies, and human-machine augmentation, with an emphasis on multimodal cloud-edge collaboration.

The launch reflects a wider trend among Chinese automakers, many of which view humanoid robots as a natural extension of capabilities developed through electric vehicle production and intelligent manufacturing. Xpeng is among the companies accelerating efforts in the sector. Earlier this month, chairman and CEO He Xiaopeng personally assumed the role of “CEO” of the company’s robotics division to speed up commercialization and development.

BYD has also confirmed that it is developing humanoid robots and is exploring the possibility of selling them through its extensive dealer network in the future. Meanwhile, Chery-backed Aimoga has moved beyond development and into commercialization. The company has begun selling humanoid robots directly to consumers, with a retail price of 285,800 yuan (approximately $42,260).

As Chinese automakers continue investing in embodied AI, humanoid robots are increasingly becoming the next frontier of competition, transforming car manufacturers into major players in the rapidly evolving robotics industry.

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