Embodied AI refers to artificial intelligence systems integrated into physical entities, particularly humanoid robots, that can interact with and learn from their environments. By leveraging technologies such as sensors, motors, machine learning, and natural language processing, these systems can perceive, act, and collaborate with their surroundings. This enables humanoid robots to adapt, improve their behavior over time, and perform tasks in a more human-like and intuitive manner.
But where are we in the development of it?
Moravec’s Paradox taught us a valuable lesson. Articulated in the 1980s by roboticist Hans Moravec, along with Marvin Minsky and Rodney Brooks, pioneers in AI and robotics.
They observed that AI systems were excelling in tasks requiring abstract reasoning but were significantly underperforming in tasks requiring real-world interactions.
The paradox is rooted in evolutionary biology.
Skills like perception and motor control have been honed over millions of years in humans and animals, making them deeply ingrained and unconscious.
By contrast, abstract reasoning and formal problem-solving are recent evolutionary developments, requiring conscious effort and learning.
The paradox was confirmed when ChatGPT started to showcase advanced cognitive skills by 2022, while robotics still struggled.
And yet now, also thanks to advancements towards embodied AI, which is integrating perception, movement, and adaptability, we’re getting closer to a “ChatGPT moment” for robotics!
A timeline of how we got here!
• 1950s-60s: AI beginnings with rule-based systems like early chess algorithms, emphasizing abstract reasoning.
• 1980s: Moravec’s Paradox articulated, emphasizing the difficulty of sensorimotor tasks for AI.
• 1997: IBM’s Deep Blue defeats Garry Kasparov in chess, showcasing AI’s success in structured problem-solving.
• 2010s: Neural networks power breakthroughs in perception (e.g., facial recognition, AlphaGo’s success in Go).
• 2020s: ChatGPT and similar models achieve advanced natural language processing, demonstrating cognitive AI’s potential.
• Present Day: Embodied AI integrates robotics with machine learning for tasks like object manipulation and real-world navigation.
• Spacial Intelligence and Embodied AI: Robots might gain human-like dexterity and adaptability, blending AI cognition with physical interaction for dynamic, real-world applications.
