AV playbook for robotics: what transfers and what doesn't

Autonomous-vehicle development offers general robotics valuable data practices—long-tail analysis, failure mining, deliberate curation, simulation infrastructure, and closed-loop deployment feedback—but not a directly reusable system design because robotics spans diverse tasks, embodiments, sensors, and action spaces. In robotics, important edge cases often arise from combinations of robot state, action history, contact conditions, and recoverability rather than unusual visual objects, making policy-generated failures, teleoperator interventions, and recovery trajectories especially informative training data. Data curation is more difficult than in driving because heterogeneous sources must be normalized and selected carefully to avoid negative transfer between capabilities or robot types. Simulation remains useful for coverage, pretraining, controlled testing, and regression evaluation, particularly in locomotion, navigation, and some rigid-body tasks, but its reliability declines for dexterous, contact-rich manipulation involving friction, deformable materials, touch, and hardware-specific behavior. Rather than relying only on a homogeneous robot fleet, robotics learning systems should integrate evidence from autonomous rollouts, teleoperation, human video, simulation, cross-embodiment data, and production deployments, using a continuous loop that identifies weaknesses, retrieves relevant episodes, evaluates improvements, and redeploys updated policies.

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