FOCUS ON AI
Top and below right : Industrial robotics at Messe Hannover 2025 .
Image : IPTC / Messe Hannover .
Image : Shutterstock . com . complex situations , analyse tasks , break them down , determine optimal action sequences and anticipate problems .
■ Reinforcement Learning ( RL ) enhanced by GenAI : Generative models can create more realistic and dynamic training environments for RL agents , allowing them to learn complex behaviours and generalise better to realworld scenarios .
Physical AI represents the ultimate goal : the seamless integration of perception , agentic reasoning and physical embodiment . It is about intelligently acting in the physical world , not just reacting to it . Generative and Agentic AI provide the cognitive foundation , the ‘ brain ’, empowering robots to operate autonomously and effectively in real-world settings .
Several key players are driving this progress , developing the necessary hardware and software ecosystems for training and deploying Physical AI . Examples include NVIDIA ’ s “ Project GR00T ” which aims to create a general-purpose foundation model for humanoid robots . Its Omniverse Isaac platform allows for simulated training of these robot ‘ brains ’ and facilitates a connection to their physical counterparts , enabling a closed loop learning system . Similarly , Tesla ’ s Optimus humanoid robot demonstrates significant advancements in dexterity and control capabilities , powered by AI trained on real-world data .
These efforts , along with those of other robotics and AI companies , are collectively pushing the boundaries of what is possible , each contributing unique approaches and technologies to the development of truly embodied AI . This is rapidly accelerating the
arrival of a future where Physical AI becomes a realistic scenario .
Transformative impact of Physical AI
Physical AI would unlock a new era of industrial automation . Here are just some examples :
■ Truly collaborative robots ( Cobots ): Robots working alongside humans as true collaborators , understanding human intentions , anticipating needs and adapting behaviour accordingly .
■ Autonomous factories : Factories of the future could be largely autonomous , with Physical AI-powered robots handling tasks from assembly and
inspection to logistics and maintenance , with minimal human intervention .
■ Adaptive manufacturing : Robots quickly reconfiguring themselves to produce different products or handle variations in materials , leading to more flexible and agile manufacturing .
■ Self-optimising systems : Physical AI robots continuously monitoring their performance , identifying areas for improvement and optimising actions over time .
Challenges and actions
While the potential of Physical AI is immense , significant challenges and considerations must be addressed .
Image : IPTC / Messe Hannover .
24 | ismr . net | ISMR February 2025