Posts Tagged ‘AutonomousSystems’
[DevoxxGR2026] Code That Moves the World: The Rise of Physical AI
Lecturer
Will Sentance is the founder of Standard Material and Codesmith, organizations at the forefront of physical AI infrastructure and AI/software engineering education. A speaker, educator, and practitioner, Sentance bridges software engineering expertise with emerging robotics and autonomous systems. He contributes to research at Oxford and leads initiatives training talent for the next wave of intelligent physical systems.
Abstract
In this forward-looking keynote at Devoxx Greece 2026, Will Sentance explores the profound convergence of software engineering and physical intelligence. Robots and autonomous systems are transitioning from specialized, brittle demonstrations to capable, generalizable agents operating in real-world environments. Sentance details the technological breakthroughs in hardware, data, and foundation models driving this transformation and argues that traditional software engineering skills are central to building the platforms, data pipelines, and integrations required for scalable physical AI deployment.
The Remarkable Progress in Physical Intelligence
Physical AI—systems that sense, understand, and act upon the physical world—has advanced dramatically. Robots now follow natural language instructions, handle novel objects, and demonstrate emergent capabilities. Foundation models for robotics enable zero-shot generalization and long-horizon planning across diverse embodiments.
Companies like Physical Intelligence, Agility Robotics, and others are moving from laboratory experiments to industrial and domestic applications. This shift is fueled by massive investment and rapid iteration.
Core Technological Enablers
Three key areas have transformed the landscape:
Hardware Revolution: Affordable, off-the-shelf components—from full humanoids to grippers and sensors—dramatically lower barriers. Edge computing platforms provide sufficient power for onboard inference.
Data Explosion: Teleoperation, simulation (including sophisticated world models), and real-world deployment generate multimodal datasets at unprecedented scale. Techniques like action chunking address real-time requirements.
AI Models: End-to-end learning replaces traditional control theory. Vision-language-action models predict continuous action trajectories, enabling flexible behavior without exhaustive manual programming.
The Physical AI Technology Stack
Sentance outlines a layered architecture:
- Real-time Control: Low-level, deterministic operations managing actuators and safety at high frequency.
- Platform and Middleware: Abstractions like ROS providing integration, simulation interfaces, and developer tools.
- Intelligence Layer: Foundation models processing vision, language, and proprioception to generate actions.
- Data and Learning Loop: Continuous collection, training, evaluation, and deployment cycle.
Opportunities for Software Engineers
Contrary to initial impressions, software engineers are perfectly positioned to lead this revolution. Approximately 80% of the required work involves familiar disciplines: systems architecture, platform engineering, data pipelines, low-level optimization, and agentic integration.
Roles at leading organizations emphasize scalable frameworks, reliable deployment, observability, and integration of AI models into production—skills honed in cloud-native and distributed systems development.
New challenges center on real-time constraints, physical dynamics, and managing massive multimodal datasets, but these build directly upon existing expertise.
Getting Started with Physical AI
Sentance encourages practical experimentation using affordable hardware like the SO-101 and open tools. Developers can quickly train policies for simple tasks such as closing a laptop lid, experiencing the full cycle from data collection to deployment.
The physical world represents the next major platform for code. Software engineers who embrace this frontier will shape the coming industrial transformation.