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Reinforcement learning (RL) is a machine learning approach where an agent learns by interacting with an environment and improving through rewards, penalties, and trial-and-error experience. Instead of being explicitly programmed with every behavior, the system gradually discovers which actions lead to better outcomes. Positive outcomes…

Predictive processing is a brain-inspired framework where an intelligent system continuously predicts incoming sensory information and updates its internal models based on prediction errors. Instead of passively reacting to the world, the system actively anticipates what it expects to see, hear, or feel next. When…

World models are internal predictive systems that allow embodied AI agents to simulate, predict, and reason about how the environment may change in response to actions. They function like an internal mental simulation engine. Instead of reacting blindly to the world moment by moment, a…

Embodied neural networks are AI models designed to learn through physical interaction with the world rather than relying only on text, static images, or abstract data. These networks connect perception, movement, learning, and decision-making into a continuous sensorimotor process. Instead of simply analyzing information passively,…

Proprioception is a robot’s ability to sense the position, movement, and force of its own body. It is often compared to the human “sixth sense” because it allows an embodied system to know where its limbs and joints are without directly looking at them. Balance…