A Nonasymptotic Theory of Gain-Dependent Error Dynamics in Behavior Cloning
Junghoon Seo

TL;DR
This paper develops a nonasymptotic, gain-dependent theoretical framework for understanding error dynamics in behavior cloning for robots, linking controller gains to failure probabilities and performance.
Contribution
It introduces a finite-horizon analysis connecting controller gains to error propagation and failure risk, extending previous asymptotic theories.
Findings
Error errors propagate as sub-Gaussian position errors governed by a gain-dependent proxy matrix.
Failure probability factorizes into a gain-dependent amplification index and validation loss.
The scalar second-order PD system's stationary variance is monotone in stiffness and damping.
Abstract
Behavior cloning (BC) policies on position-controlled robots inherit the closed-loop response of the underlying PD controller, yet the nonasymptotic finite-horizon consequences of controller gains for BC failure remain open. We show that independent sub-Gaussian action errors propagate through the gain-dependent closed-loop dynamics to yield sub-Gaussian position errors whose proxy matrix governs the failure tail. The probability of horizon- task failure factorizes into a gain-dependent amplification index and the validation loss plus a generalization slack, so training loss alone cannot predict closed-loop performance. Under shape-preserving upper-bound structural assumptions, the proxy admits the scalar bound , with decomposed into label difficulty, injection strength, and contraction. This ranks the four…
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