DXresearch
Humanoid robotics is entering the same industrialization phase once seen in automotive and aviation: innovation alone is no longer enough. Standards such as ISO 10218, ISO/TS 15066, IEC 61508, IEC 62443 and emerging humanoid-focused frameworks will define how Physical AI becomes scalable, certifiable and insurable. The next competitive advantage is trusted deployment. That is where…
Lyra did not begin as a machine that could move, perceive, or decide, but as a convergence point for a set of engineering assumptions that had not yet been tested under real-world conditions where timing, uncertainty, and physical interaction define system behavior. Dirk stood in front of the first assembled system with the awareness that…
Robotics learning is scaling faster than robotics talent 📊 Market Access to robotics education has expanded rapidly through open courses from leading institutions. Platforms from Stanford University, Massachusetts Institute of Technology, and ETH Zürich provide structured, high-quality content at global scale (https://see.stanford.edu/Course/CS223A) ⚙️ Technology Robotics capability emerges from the interaction of perception, compute, actuation, and…
A practitioner’s view from physical AI Working at Infineon Technologies and focusing on physical AI, I see humanoid robotics moving into a more defined market phase. The past weeks brought a shift in signals. Discussions are becoming more grounded. Execution is gaining weight. Early production announcements, ecosystem partnerships, and policy discussions now shape the narrative.…
Robotics is not a single global market. It is a set of regionally differentiated ecosystems, each with distinct strengths, cluster structures, and strategic trajectories. Across all regions, the most important structural pattern is that robotics capability concentrates geographically. Cities and corridors matter more than national averages. Understanding where capability concentrates — and why — is…
Why system architecture, not intelligence, decides whether humanoids scale Humanoid robots concentrate more actuation, sensing, and compute per kilogram than almost any other engineered system. They are mobile, contact-rich, power-constrained, and expected to operate safely around humans while continuously evolving through software updates. In this regime, electrical and electronic architecture is not a background discipline.…
Torque sensing has transitioned from a niche capability to a foundational system primitive for physical-AI robots operating in unstructured, contact-rich environments. By exposing interaction forces directly at the joint or actuator level, torque sensing enables safer manipulation, more stable locomotion, improved disturbance rejection, and learning policies that generalize better outside controlled settings. Compared with position-dominant…
A humanoid Physical AI system is not defined by its shape, but by the tight coupling of perception, intelligence, control, actuation, energy, and safety within a single embodied machine. Unlike task-specific robots, humanoids must integrate all major functional blocks at human scale, under continuous interaction with people and environments not designed for automation. This chapter…
Physical AI refers to embodied systems that sense, decide, and act in the real world using a combination of learning-based intelligence and deterministic physical control. It is not defined by a specific algorithm or model class, but by a system property: intelligence that is inseparable from physics, timing, energy, and safety constraints. Physical AI systems…
The transition from classical robotics to Physical AI represents a structural change in how intelligence, control, and uncertainty are handled inside robotic systems. Classical robots achieve reliability by limiting scope, enforcing determinism, and relying on explicit models. Physical AI systems expand capability by embedding learning-driven perception and decision layers, but in doing so they…