Understanding Moravec's Paradox

Summary

Understanding Moravec's Paradox Aug 17 2025 Morevec's paradox is a little weird in a few ways. First it's not a paradox, and second it's widely miss-interpreted. At its core, Moravec's paradox is the observation that reasoning takes much less computation compared to sensorimotor and perception tasks. It's often (incorrectly) described as tasks that are easy for humans are difficult for machines and visa versa. The answer from the human's side is relatively simple to explain. As hypothesised by Morvec, humans have evolved to be good at tasks that benefit them in survival, such as fine motor control and vision. But this doesn't really explain why machines find certain problems easy or difficult, which is the part of this observation that I want to focus on here. The Key idea I believe that this observation can be broken down into two components: search space and reward sparsity. In general, ignoring humans or machines, problems are more difficult if their search space is large, and reward signals are sparse. We can take chess as an example. Something that is difficult for humans, since we never evolved to play chess or quite reason in that way. However, machines excel at playing chess. When viewed through the lens of search and rewards, this becomes more clear. Firstly, the average number of moves in a chess game is around 40, and the average branching factor (possible legal moves per state) is 35. Whilst this still is a large search space, compared to other tasks this is relatively small. Additionally, rewards are quite common, either produced via an evaluation function (not perfect) or by waiting until a terminal state is reached. Now compare this to a task such as robotics. A bipedal robot will have various actuators for each limb (say 3 per limb, many more for hands/fingers), each actuator can move between 50 to a few thousand times per second. Not only is the action space large, but the environment which the robot operates in is complex, and it could take tens of...