Previously, autonomous machines were unable to perceive and sense the world around them. But with generative physical AI, robots can be built and trained to seamlessly interact with and adapt to their surroundings in the real world.
To build physical AI, teams need powerful, physics-based simulations that provide a safe, controlled environment for training autonomous machines. This not only enhances the efficiency and accuracy of robots in performing complex tasks, but also facilitates more natural interactions between humans and machines, improving accessibility and functionality in real-world applications.
Generative physical AI is unlocking new capabilities that will transform every industry. For example:
Robots: With physical AI, robots demonstrate significant advancements in operational capabilities within various settings.
- Autonomous Mobile Robots (AMRs) in warehouses can navigate complex environments and avoid obstacles, including humans, by using direct feedback from onboard sensors.
- Manipulators can adjust their grasping strength and position based on the pose of objects on a conveyor belt, showcasing both fine and gross motor skills tailored to the object type.
- Surgical robots benefit from this technology by learning intricate tasks, such as threading needles and performing stitches, highlighting the precision and adaptability of generative physical AI in training robots for specialized tasks.
Autonomous Vehicles (AVs): AVs use sensors to perceive and understand their surroundings, enabling them to make informed decisions in various environments, from open freeways to urban cityscapes. By training AVs on physical AI, it allows the AVs to more accurately detect pedestrians, respond to traffic or weather conditions, and autonomously navigate lane changes, effectively adapting to a wide range of unexpected scenarios.
Smart Spaces: Physical AI is enhancing the functionality and safety of large indoor spaces like factories and warehouses, where daily activities involve a steady traffic of people, vehicles, and robots. Using fixed cameras and advanced computer vision models, teams can enhance dynamic route planning and optimize operational efficiency by tracking multiple entities and activities within these spaces. Additionally, they prioritize human safety by accurately perceiving and understanding complex, large-scale environments.