The symbol grounding problem asks how abstract symbols — words, concepts, or representations — acquire real meaning without being connected to actual experiences in the physical world. Proposed by philosopher Stevan Harnad in 1990, it highlights a core limitation of purely symbolic AI: a system…
Continue reading...Developmental robotics studies how machines can acquire skills incrementally, much like human infants, through exploration, play, and social interaction. Rather than programming everything at once or training on massive datasets from the start, this approach lets robots learn gradually — starting with simple movements and…
Continue reading...Ecological perception views perception as directly picking up opportunities for action (affordances) from the structured environment, rather than building complex internal reconstructions of the world. Inspired by psychologist J.J. Gibson’s work, this approach suggests that the environment is rich with information that agents can detect…
Continue reading...Grounded AI connects abstract representations and symbols (such as words, concepts, or plans) to real sensory and motor experiences in the physical world. It directly addresses a major limitation of purely symbolic or statistical AI systems: they can manipulate symbols fluently but often lack genuine…
Continue reading...Understanding Progress in Embodied AI: The Five Levels of Embodied AGI This page provides a general overview of a useful framework for thinking about progress toward advanced physical AI systems. The five-level taxonomy was proposed by researchers Yequan Wang and Aixin Sun in their 2025…
Continue reading...