Embodied Cognitive Science is an interdisciplinary field combining elements from cognitive science, artificial intelligence, and robotics to study the connection between the body and cognitive processing. This approach suggests that our bodily interactions with the environment plays a crucial role in shaping our thoughts and cognition.


Imagine playing a video game. It’s much easier to play and have fun when you can control your character - you can make it jump, run, or pick up objects. Now, replace your character with a robot, and instead of a game, it’s the real world. The robot needs to know how to interact within the world to understand it better. That’s what Embodied Cognitive Science is about - learning by doing.

In-depth explanation

Embodied Cognitive Science posits that cognition cannot be separated from the body and the environment. In other words, it postulates that the body isn’t just a container for the brain, but an integral part of our cognitive systems.

Embodied cognition breaks down into four main theories:

  1. Cognition significantly depends on the body’s aspects and features.
  2. Cognition is situated in physical and social contexts that constantly interact.
  3. Cognition is time-pressed; it doesn’t happen in the abstract, but under conditions that require rapid responses.
  4. We offload cognitive work onto the environment, using it to aid in our cognitive processes.

In artificial intelligence, Embodied Cognitive Science plays a significant role in developing robots and AI systems capable of interacting with their environment. Embodied AI uses sensors and actuators (body parts) to interact with the physical world, helping them better understand and learn from their environment. This interaction serves two purposes: it enables the machine to adapt to the environment and also generate new knowledge that was otherwise inaccessible.

A primary example of embodied cognition in AI is in the field of deep reinforcement learning - where an agent learns to take actions based on its interaction with the environment. For instance, consider a autonomous vehicle learning to navigate the roads: it must perceive the environment, make rapid decisions based on its current state, and learn from the consequences of its actions to improve future decision-making.

There is also a growing interest in embodied language learning in AI, where an agent learns language through interaction with the environment, similar to a child learning language through interactions with their surroundings.

Reinforcement Learning, Robot Learning, Sensorimotor Skills, Swarm Intelligence, Distributed Artificial Intelligence, Physical grounding, Situated Cognition, Embodied AI, Embodied language learning