Examining how robots are moving from demonstrations to daily use.
Updated
January 28, 2026 5:53 PM
An industrial robotic arm capable of autonomous welding. PHOTO: ADOBE STOCK
CES 2026 did not frame robotics as a distant future or a technological spectacle. Instead, it highlighted machines designed for the slow, practical work of fitting into human systems. Across the show floor, robots were no longer performing for attention but being shaped by real-world constraints—space, safety, fatigue and repetition.
They appeared in factories, homes, emergency settings and industrial sites, each responding to a specific kind of human limitation. Together, these four robots reveal how robotics is being redefined: not as a replacement for people, but as infrastructure that quietly takes on work humans are least meant to carry alone.
Hyundai Motor unveiled its electric humanoid robot, Atlas, during a media day on January 5, 2026, at the Mandalay Bay Convention Center in Las Vegas as part of CES 2026. Developed with Boston Dynamics, Hyundai’s U.S.-based robotics subsidiary, Atlas was presented in two forms: a research prototype and a commercial model designed for real factory environments.
Shown under the theme “AI Robotics, Beyond the Lab to Life: Partnering Human Progress,” Atlas is designed to work alongside humans rather than replace them. The premise is straightforward—robots take on physically demanding and repetitive tasks such as sorting and assembly, while people focus on work requiring judgment, creativity and decision-making.
Built for industrial use, the commercial version of Atlas is designed to adapt quickly, with Hyundai stating it can learn new tasks within a day. Its adult-sized humanoid form features 56 degrees of freedom, enabling flexible, human-like movement. Tactile sensors in its hands and a 360-degree vision system support spatial awareness and precise operation.
Atlas is also engineered for demanding conditions. It can lift up to 50 kilograms, operate in temperatures ranging from –20°C to 40°C and is waterproof, making it suitable for challenging factory settings.
Looking ahead, Hyundai expects Atlas to begin with parts sorting and sequencing by 2028, move into assembly by 2030 and later take on precision tasks that require sustained physical effort and focus.
Widemount’s Smart Firefighting Robot is designed to operate in environments that are difficult and dangerous for humans to enter. Developed by Widemount Dynamics, a spinout from the Hong Kong Polytechnic University, the robot is built to support emergency teams during fires, particularly in enclosed and smoke-filled spaces.
The robot can move through buildings and industrial facilities even when visibility is near zero. Rather than relying on cameras or GPS, it uses radar-based mapping to understand its surroundings and determine a safe path forward. This allows it to continue operating when smoke, heat or debris would normally restrict access.
As it approaches a fire, the robot analyses the burning object. Its onboard AI helps identify the material involved and selects an appropriate extinguishing method. Sensors simultaneously assess flame intensity and send real-time updates to command centres, giving responders clearer situational awareness.
When actively fighting a fire, the robot can aim directly at the source and deploy extinguishing agents autonomously. The system continuously adjusts its actions based on incoming sensor data, reducing the need for constant human intervention during high-risk situations.
At CES 2026, LG Electronics offered a glimpse into how household work could gradually shift from people to machines. The company introduced LG CLOiD, an AI-powered home robot designed to manage everyday chores by working directly with connected appliances within LG’s ThinQ ecosystem.
Designed for indoor living spaces, CLOiD features a compact upper body with two articulated arms, a head unit and a wheeled base that enables steady movement across floors. Its torso can tilt to adjust height, allowing it to reach items placed low or on kitchen counters. The arms and hands are built for careful handling, enabling the robot to grip common household objects rather than heavy tools. The head also functions as a mobile control unit, housing cameras, sensors, a display and voice interaction capabilities for communication and monitoring.
In practice, CLOiD acts as a task coordinator. It can retrieve items from appliances, operate ovens and washing machines and manage laundry cycles from start to finish, including folding and stacking clothes. By connecting multiple devices through the ThinQ system, the robot turns separate appliances into a single, coordinated workflow.
These capabilities are supported by LG’s Physical AI system. CLOiD uses vision to recognise objects and interpret its surroundings, language processing to understand instructions and action control to execute tasks step by step. Together, these systems allow the robot to follow routines, respond to user input and adjust task execution over time.
Doosan Robotics introduced Scan & Go at CES 2026, an AI-driven robotic system designed to automate large-scale surface repair and inspection. The solution targets environments with complex, irregular surfaces that are difficult to pre-program, such as aircraft structures, wind turbine blades and large industrial installations.
Scan & Go operates by scanning surfaces on site and building an understanding of their shape in real time. Instead of relying on detailed digital models or manual coding, the system plans its movements based on live data. This enables it to adapt to variations in size, curvature and surface condition without extensive setup.
The underlying technology combines 3D sensing with AI-based motion planning. The system interprets surface data, generates tool paths and refines its actions as work progresses. In practical terms, this reduces manual intervention while maintaining consistency across large work areas.
By handling surface preparation and inspection tasks that are time-consuming and physically demanding, Scan & Go is positioned as a support tool for industrial teams operating at scale.
Taken together, these robots signal a clear shift in how machines are being designed and deployed. Across factories, homes, emergency sites and industrial infrastructure, robotics is moving beyond demonstrations and into practical roles that support human work.
The unifying theme is not replacement, but relief—robots taking on tasks that are repetitive, hazardous or physically demanding. CES 2026 suggests that robotics is evolving from spectacle to utility, with a growing focus on systems that adapt to real environments, respond to genuine constraints and integrate into everyday workflows.
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AI growth is increasingly becoming a manufacturing, packaging and deployment challenge — not just a computing one.
Updated
May 26, 2026 5:28 PM
Taipei 101 and Taipei Nan Shan Plaza, viewed from Elephant Mountain. PHOTO: UNSPLASH
As AI companies continue scaling larger models and data centers, the pressure is no longer falling only on chip design. Manufacturing capacity, advanced packaging and infrastructure deployment are becoming equally important parts of the AI race. AMD’s latest investment announcement reflects how quickly that shift is accelerating.
The US chipmaker announced plans to invest more than US$10 billion across Taiwan’s semiconductor and manufacturing ecosystem to support next-generation AI infrastructure. The investment focuses on expanding partnerships and increasing advanced packaging capacity needed for future AI systems.
The announcement highlights a growing reality across the AI industry. Building powerful AI chips is no longer enough on its own. Companies now also need the manufacturing networks, packaging technologies and supply chain coordination required to deploy AI infrastructure at global scale.
AMD’s investments center heavily around advanced chip packaging, an area becoming increasingly critical as AI systems demand higher performance and greater power efficiency. Traditional chip architectures are struggling to keep pace with the size and complexity of modern AI workloads. Advanced packaging helps connect processors, memory and computing systems more efficiently while managing power and cooling limitations inside large-scale AI environments.
The company said it is working with Taiwan-based partners including ASE, SPIL and PTI to develop next-generation packaging technologies for its upcoming 6th Gen AMD EPYC processors, codenamed “Venice.” AMD also said it had qualified what it described as the industry’s first 2.5D panel-based EFB interconnect technology alongside PTI.
At the center of the broader strategy is AMD Helios, the company’s rack-scale AI infrastructure platform scheduled for deployment beginning in the second half of 2026. The platform combines AMD Instinct MI450X GPUs, 6th Gen EPYC CPUs, networking systems and AMD’s ROCm software stack into integrated AI infrastructure systems designed for hyperscale deployment.
Rather than selling individual processors alone, companies are increasingly building complete AI infrastructure platforms that combine hardware, software, cooling systems and power management into unified deployments. That transition is reshaping how AI infrastructure is designed, manufactured and delivered.
Taiwan is also becoming more deeply embedded in that process. AMD’s investment spans not only semiconductor packaging companies but also manufacturing and system integration partners including Sanmina, Wiwynn, Wistron and Inventec. The partnerships reflect Taiwan’s growing role as one of the operational centers of the global AI infrastructure economy.
Dr. Lisa Su, Chair and CEO of AMD, said: “As AI adoption accelerates, our global customers are rapidly scaling AI infrastructure to meet growing compute demand. By combining AMD leadership in high-performance computing with the Taiwan ecosystem and our strategic global partners, we are enabling integrated, rack-scale AI infrastructure that helps customers accelerate deployment of next-generation AI systems”.
Power efficiency is becoming another major challenge shaping AI infrastructure decisions. As AI workloads consume more electricity and generate more heat, infrastructure providers are increasingly being forced to rethink cooling systems, interconnect technologies and deployment economics.
AMD’s announcement signals how the AI competition is evolving beyond model development and raw computing power. The next stage may depend just as heavily on who can manufacture, package and deploy AI infrastructure fast enough to support global demand.