Viper: Verifiable Imitation Learning Policy for Efficient Robotic Manipulation

Abstract

Imitation learning (IL) presents a promising paradigm for enabling embodied robots to efficiently acquire human-like manipulation skills. However, prevailing methods face a persistent trade-off between motion precision and computational tractability. To resolve this fundamental challenge, this paper introduces Viper, a framework for Verifiable Imitation learning Policy for Efficient Robotic manipulation. Viper integrates principles of Nonlinear Model Predictive Control (NMPC) within a learning-based model. Grounded in an NMPC-style closed-loop architecture, the proposed method unifies the modeling of nonlinear system dynamics with online, multi-horizon optimization of state-action predictions, while intrinsically embedding physical constraints. This co-design enables both smooth trajectory generation and fast execution. Furthermore, a theoretical stability analysis for the Viper framework is provided. Extensive evaluations, from simulated benchmarks to real-world manipulation tasks, demonstrate that Viper effectively reconciles the competing demands of precision and speed inherent in existing robotic IL paradigms. The source code will be released upon paper acceptance.