The term “embodied artificial intelligence” has become a buzzword in robotics and AI research. Basically it refers to embedding an AI system into a machine, as we’re sure you guessed or knew.
But let’s deconstruct it a bit and get into the details of what it actually means. At its core, embodied artificial intelligence refers to the idea that intelligence in artificial systems is not solely a product of abstract computation.
Instead, it emerges from the interaction of an AI-equipped physical entity with its environment. This concept underscores the inseparability of physicality, sensory input, and environmental interaction in the manifestation of intelligent behaviour in machines.
To coin a phrase
The phrase “embodied artificial intelligence”, or embodied AI for shot, builds upon the broader concept of embodied intelligence, which was popularised some years ago by researchers like Rodney Brooks.
Brooks, a pioneer in behaviour-based robotics, argued that intelligence could not be understood or achieved without grounding it in physical interaction with the real world. This marked a departure from traditional AI, which focused heavily on symbolic reasoning and abstract problem-solving without physical integration.
In the context of robotics, the term “embodied artificial intelligence” highlights the importance of designing AI systems that leverage their physical form to interact with and adapt to their surroundings. This shift has proven crucial in developing more sophisticated and capable robots.
Examples of embodied AI
Embodied artificial intelligence can be observed in various cutting-edge robotic systems:
- Boston Dynamics robots: Robots like Spot and Atlas exemplify embodied AI. Their ability to navigate complex terrains or perform acrobatic manoeuvres stems from the integration of sensory inputs, motor functions, and real-time environmental feedback.
- Autonomous vehicles: Self-driving cars rely on embodied AI principles to interact with their environment. Sensors, cameras, and real-time processing enable them to perceive roads, traffic, and obstacles, making split-second decisions to ensure safe navigation.
- Humanoid robots: Robots such as Tesla’s Optimus or Figure AI’s humanoid prototypes use their physical design and AI to perform tasks that require human-like interaction with the physical world, such as picking up objects or navigating human environments.
What is intelligence in AI?
The term “intelligence” in artificial systems is multifaceted. In the context of AI, intelligence is often defined as the ability to learn, understand, and apply knowledge to solve problems or adapt to new environments.
While traditional AI focused on abstract problem-solving, embodied AI expands this definition to include the physicality and situational awareness necessary for robots to function effectively in the real world.
Measuring artificial intelligence
Can artificial intelligence truly be measured? While most people would accept that the IQ test is limited in its ability to measure intelligence because it mainly measure logical-mathematical and reasoning abilities, something is better than nothing.
In traditional AI, performance metrics often focus on task-specific benchmarks—such as a machine’s ability to win a game of chess or recognise objects in images. Embodied AI, however, requires additional evaluation methods that assess:
- Adaptability: How well does the AI handle new or unforeseen situations?
- Physical performance: Can the AI effectively manipulate objects, navigate spaces, or interact with humans?
- Sensorimotor integration: How seamlessly does the AI combine sensory input with physical action?
These metrics reflect the unique challenges and opportunities presented by embodied AI systems.
At the moment, probably the most widely accepted way of measuring an AI system’s performance is through the “TOPS” method – trillions of operations per second (TOPS). Some might say this is a crude measure, but at least it’s easy to understand.
The Linpack benchmark is used to measure supercomputers’ computational abilities in FLOPS (Floating Point Operation Per Second). The world’s most powerful computer, El Capitan at Lawrence Livermore National Laboratory, can operate at 1.742 exaflops – that’s 1,742,000,000,000,000,000 flops. I think.
Other measures for computing performance, whether AI computers or not, are all debatable – and in the past couple of years, they have indeed been debated by NIST and others, and we should see some results soon.
Artificial intelligence versus human intelligence
The question of whether embodied AI can achieve “true intelligence” is an ongoing debate. Current systems excel in specific tasks but lack the general adaptability and self-awareness that characterise human intelligence.
However, embodied AI is narrowing this gap by emphasising real-world interaction, a key component of intelligent behaviour.
Unlike humans, whose intelligence is deeply rooted in abstract thinking and cultural learning, embodied AI focuses on practical functionality. For example, a humanoid robot might outperform a human in repetitive manual tasks or in environments designed for robots, but it remains limited in creative and emotional domains.
Frontier AI
The study and development of embodied artificial intelligence represent a critical frontier in robotics and AI. By integrating physical interaction with sensory perception and decision-making, embodied AI systems are becoming increasingly capable and versatile.
For people developing AI systems or working in the sector, understanding this concept may be key to staying ahead in a rapidly evolving field.
While the term “embodied artificial intelligence” may initially seem like trendy jargon, it encapsulates a vital shift in how we design and conceptualise intelligent systems. As robotics and AI continue to advance, embodied AI will play a pivotal role in bridging the gap between machines and the real world.
