Robot Program Parameter Inference via Differentiable Shadow Program Inversion

TL;DR

This paper introduces Shadow Program Inversion (SPI), a novel method that uses differentiable shadow programs and gradient-based optimization to efficiently infer robot skill parameters directly from data, enabling zero-shot generalization across tasks.

Contribution

SPI is the first approach to leverage differentiable shadow programs for parameter inference, allowing efficient, zero-shot adaptation of robot skills without retraining.

Findings

Successfully infers parameters for non-differentiable skills

Enables zero-shot generalization across task variants

Validated on three robots in industrial and household scenarios

Abstract

Challenging manipulation tasks can be solved effectively by combining individual robot skills, which must be parameterized for the concrete physical environment and task at hand. This is time-consuming and difficult for human programmers, particularly for force-controlled skills. To this end, we present Shadow Program Inversion (SPI), a novel approach to infer optimal skill parameters directly from data. SPI leverages unsupervised learning to train an auxiliary differentiable program representation ("shadow program") and realizes parameter inference via gradient-based model inversion. Our method enables the use of efficient first-order optimizers to infer optimal parameters for originally non-differentiable skills, including many skill variants currently used in production. SPI zero-shot generalizes across task objectives, meaning that shadow programs do not need to be retrained to infer parameters for different task variants. We evaluate our methods on three different robots and skill frameworks in industrial and household scenarios. Code and examples are available at https://innolab.artiminds.com/icra2021.