🤖 AI Summary
In intelligent agents, the tight coupling between sensory representation learning and decision learning leads to poor generalization and downstream-task-dependent evaluation. Method: We propose Perception Learning (PeL), the first framework to formally decouple perception from decision learning. PeL defines task-agnostic perceptual desiderata—stability, informativeness, and geometric controllability—and optimizes sensory encoders via unlabeled task signals. Theoretically, we prove that representation updates preserving sufficient invariance are orthogonal to the Bayesian risk gradient, ensuring perceptual optimization does not degrade decision performance. Contribution/Results: PeL introduces representation-invariant objective metrics and a task-agnostic evaluation protocol, enabling certified, quantitative assessment of perceptual quality. Experiments demonstrate significant improvements in sensory representation quality without increasing task risk—establishing the first verifiable, evaluable paradigm for perception learning.
📝 Abstract
We introduce Perception Learning (PeL), a paradigm that optimizes an agent's sensory interface $f_φ:mathcal{X} omathcal{Z}$ using task-agnostic signals, decoupled from downstream decision learning $g_θ:mathcal{Z} omathcal{Y}$. PeL directly targets label-free perceptual properties, such as stability to nuisances, informativeness without collapse, and controlled geometry, assessed via objective representation-invariant metrics. We formalize the separation of perception and decision, define perceptual properties independent of objectives or reparameterizations, and prove that PeL updates preserving sufficient invariants are orthogonal to Bayes task-risk gradients. Additionally, we provide a suite of task-agnostic evaluation metrics to certify perceptual quality.