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In robotics, a policy model usually refers to a model that maps perceptual input directly to action output, and is a core component of reinforcement learning or imitation learning.
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10/21/2026
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UnverifiedThe combination of physics-based models and data-driven models is referred to in robotics as 'hybrid modeling' or 'residual learning,' with the typical approach being that the physics model provides a baseline prediction while a neural network learns the residual error.75% similarUnverifiedSelf-modeling involves having the model build an internal representation of its own behavioral patterns, rather than merely predicting external tasks.72% similarUnverifiedUnlike traditional model predictive control (MPC), locomotion policy models compress complex balance and gait planning into a network capable of real-time inference, making deployment and iteration easier.70% similarVerifiedA world model refers to a robot's internal predictive capability of the physical environment—given the current state and an action, it can predict what will happen next.70% similarVerifiedThe output of a decision model is often a label or probability distribution over a finite action set, similar to a policy network in reinforcement learning, whereas generative models require autoregressive sampling over a large vocabulary.70% similar
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