Editing Ai Robotics (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
Robotics is one of AI's most challenging domains because it requires closing the '''perception-action loop in the physical world'''. Unlike image classifiers that observe but don't act, and unlike software agents whose actions are reversible, robots take physical actions in an irreversible, noisy, high-dimensional reality.

'''The robot learning paradigm''' has evolved through three approaches:

'''Classical robotics''': Hand-programmed behaviors, kinematics, and path planning. Rigid, requires precise models of the environment, works well in structured factory settings where every object is in a known position.

'''Learning from demonstration (imitation learning)''': A human teleoperates the robot to demonstrate a task. The robot learns to imitate the human's policy. This is natural and data-efficient but is bounded by the human's demonstration quality and doesn't generalize beyond demonstrated situations.

'''Reinforcement learning''': The robot learns by trial and error in simulation or the real world, optimizing a reward signal. RL has produced remarkable results — OpenAI's Dactyl solved a Rubik's Cube with a five-fingered robot hand; Boston Dynamics' Spot learns parkour. But RL in robotics is extremely sample-inefficient: real robots wear out, and physical interaction is slow. The key solution is training in simulation, then transferring to reality.

'''The sim-to-real transfer problem''' is fundamental. A policy trained in simulation learns the simulation's physics. Real physics is noisier, objects have different friction coefficients, and sensors are imperfect. Domain randomization addresses this by training with randomized simulation parameters so the policy must be robust to a range of conditions — and real physics falls within that range.

'''Foundation models for robotics''' are the newest paradigm. Models like RT-2 (Google) combine a vision-language model with robot action prediction, enabling generalization across tasks described in natural language. Given "pick up the red block and place it on the blue bowl," the robot can execute this instruction despite never seeing this exact task — because the VLM has learned general object understanding and the robot policy has learned manipulation primitives.
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