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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A look at how motivation, not metrics, is becoming the real frontier in fitness tech
Updated
February 7, 2026 2:18 PM
A group of people running together. PHOTO: FREEPIK
Most running apps focus on measurement. Distance, pace, heart rate, badges. They record activity well, but struggle to help users maintain consistency over time. As a result, many people track diligently at first, then gradually disengage.
That drop-off has pushed developers to rethink what fitness technology is actually for. Instead of just documenting activity, some platforms are now trying to influence behaviour itself. Paceful, an AI-powered running platform developed by SportsTech startup xCREW, is part of that shift — not by adding more metrics, but by focusing on how people stay consistent. The platform is built on a simple behavioural insight: most people don’t stop exercising because they don’t care about health. They stop because routines are fragile. Miss a few days and the habit collapses. Technology that focuses only on performance metrics doesn’t solve that. Systems that reinforce consistency, belonging and feedback loops might.
Instead of treating running as a solo, data-driven task, Paceful is built around two ideas: behavioural incentives and social alignment. The system turns real-world running activity into tangible rewards and it uses AI to connect runners to people, clubs and challenges that fit how and where they actually run.
At the technical level, Paceful connects with existing fitness ecosystems. Users can import workout data from platforms like Apple Health and Strava rather than starting from scratch. Once inside the system, AI models analyse pace, frequency, location and participation patterns. That data is used to recommend running partners, clubs and group challenges that match each runner’s habits and context.
What makes this approach different is not the tracking itself, but what the platform does with the data it collects. Running distance and consistency become inputs for a reward system that offers physical-world incentives, such as gear, race entries or gift cards. The idea is to link effort to something concrete, rather than abstract. The company also built the system around community logic rather than individual competition. Even solo runners are placed into challenge formats designed to simulate the motivation of a group. In practice, that means users feel part of a shared structure even when running alone.
During a six-month beta phase in the US, xCREW tested Paceful with more than 4,000 running clubs and around 50,000 runners. According to the company, users increased their running frequency significantly and weekly retention remained unusually high for a fitness platform. One beta tester summed it up this way: “Strava just logs records, but Paceful rewards you for every run, which is a completely different motivation”.
The company has raised seed funding and plans to expand the platform beyond running, walking, trekking, cycling and swimming. Instead of asking how accurately technology can measure the body, platforms like Paceful are asking a different question: how technology might influence everyday behaviour. Not by adding more data, but by shaping the conditions around effort, feedback and social connection.
As AI becomes more common in consumer products, its real impact may depend less on how advanced the models are and more on what they are applied to. In this case, the focus isn’t speed or performance — it’s consistency. And whether systems like this can meaningfully support it over time.