Ivan Hernandez Dalas - Mechatronics

The Gene.01 humanoid is making its U.S. debut this week. | Source: Generative Bionics Generative Bionics, which developing humanoid robots for industrial applications, this week unveiled Gene.01 at AMD Advancing AI 2026. Built in six months, the robot combines full-body tactile sensing with physics-native AI, said the deep tech company. The AI system running the robot integrates Gene.01’s body, mechanics, and intelligence so it can better interpret and respond to the physical world. “Unlike other one-size-fits-all humanoids, Gene.01 is a scalable platform, customized at the edge of AI, design, and end-effector level to maximize each customer’s needs,” stated Daniele Pucci, the CEO of Generative Bionics. “It is also the first expression of our belief that physical AI must be human-aware, physics-native, and industrial from Day 1.” “In six months, our team moved from concept to a fully functional and sensorized humanoid platform by combining ...

U.S. manufacturing reached a record $2.91 trillion in value. Source: MISUMI Americas Labor shortages and a desire for national self-reliance are driving reshoring of production to the U.S. But how much reshoring is actually happening? MISUMI Americas today released its report on The Rise of U.S. Manufacturing , which has compiled mostly positive statistics. Demographics is driving investment automation, as manufacturers struggle to mitigate the growing workforce challenge. The industry will need 3.8 million more workers by 2033, and 1.9 million of those positions could go unfilled, according to The Manufacturing Institute and Deloitte. Of that 3.8 million, about 2.8 million comes from workers retiring and the remainder from new growth, including jobs tied directly to the CHIPS Act, the Inflation Reduction Act, and the Infrastructure Investment and Jobs Act. More than 65% of manufacturers have cited attracting and retaining talent as their single biggest business challenge. Investm...

Contactile offers robotic hands and grippers equipped with its tactile sensors. | Source: Contactile World models are the next frontier in robot learning. But, because conditioning on touch remains fundamentally incomplete, they cannot reliably generalize across novel surfaces and objects . A new model class,  VμA , proposes to fix that by making friction a first-class input.   World models are the next frontier The most ambitious direction in robot learning today is the world model: a generalist model of physical reality that a robot can use to predict the consequences of its actions, plan across long horizons, and generalize to situations it has never encountered in training. If a robot’s internal model of the world is accurate enough, it does not need to memorize every task. Instead, it can reason its way through novel ones. This is a compelling vision, and the field is moving fast. But deploying world models in real robotic systems requires a step that receives less attention ...

Learn how to design human-robot collaboration for trust and safety in a webinar. Source: Boston Dynamics As humanoid robots start to move from laboratories into industrial and commercial environments, developers must address safety concerns. Boston Dynamics this week is presenting a free webinar to discuss the human-machine interaction, or HMI, considerations for humanoids. The key to establishing trust, safety, and productive coexistence is not making robots look human, but making them predictable, according to the company . In the webinar on “The Art Behind Human-Robot Interaction,” Boston Dynamics experts will explain the importance of HMI for human-robot collaboration. Mario Bollini, director of human-robot interaction, and Leland Hepler, UI designer and former Disney and video game animator, will explain how Boston Dynamics is merging engineering and artificial intelligence with classic animation techniques in its Atlas humanoid, which is among the few in commercial...

The AGIBOT G2 wheeled robot provided subway guidance at WAIC 2026. Source: AGIBOT Embodied AI including humanoid robots are moving toward wider production and deployment. AGIBOT yesterday unveiled four new products at the World Artificial Intelligence Conference, or WAIC. The company said they expand its portfolio across full-size humanoid platforms, heavy-payload industrial task robots, and direct-drive dexterous manipulation systems. “The industry should no longer judge embodied AI by whether a robot can complete an impressive demonstration once, but by whether it can be manufactured, delivered and integrated reliably into real operating environments,” stated Peng Zhihui, AGIBOT co-founder, president, and chief technology officer. “AGIBOT A3 Ultra, G2 Max, and OmniHand 3 Ultra-M address three essential layers of that challenge: the humanoid platform, industrial task execution, and dexterous manipulation,” he added. “Together with our recent production and factory-deploy...

Imagine you want to teach a robot to push an object on a table. The standard recipe in robot learning is to collect hundreds of expert demonstrations on a real robot, train an imitation learning policy on that data, and then evaluate the policy by running it many times on the same real robot. Both stages (data collection and evaluation) are slow, expensive, and hard to reproduce: hardware breaks, lighting changes, objects drift out of place, and every new task means more hours in the lab. A natural question is whether we can replace some of this real-robot work with a simulator. Classical physics-based simulators are powerful, but building one for a new task means manually modeling geometries, contacts, friction, and deformation, and the resulting simulator often still does not match reality closely enough for policies trained inside it to transfer. In our work, we take a different route. We build an Interactive World Simulator : a learned, action-conditioned video prediction model ...