How to Train Your Robot

Rapid progress in AI has relied on massive training datasets, but robotics lacks the enormous publicly available corpora that propelled large language models. Training a robot requires sequences showing tasks being performed so models can learn to predict or score actions; common approaches include teleoperation and behavioral cloning, where humans directly control robots to generate examples. Teleoperation data matches real robot capabilities but is expensive, awkward, and often low quality because operators lack tactile feedback. An alternative is collecting autonomous robot behavior and scoring it via reinforcement learning - historic “arm farm” setups produced useful grasping data but scale and diversity remain limited, since deployed robots today are few and their repeated real-world tasks generate highly redundant data.

Simulation provides the main route to scale: virtual robots produce vast, cheap experience and enable teacher-student and RL training at fleet scale. High-profile successes include simulated training for locomotion and OpenAI’s Rubik’s Cube hand (equivalent to thousands of simulated years) and Waymo’s billions of simulated miles versus millions driven. The key limitation is the sim-to-real gap, especially for complex manipulation of soft/deformable objects, so teams use domain randomization and Automatic Domain Randomization to expand simulated variation; even then transfer performance can be modest (e.g., low success rates on hardest Rubik configurations). Synthetic data generation from real demonstrations and human-to-robot mapping remain complementary strategies while the field pursues deployment-driven “data flywheels.”