Inference-Time Enhancement of Generative Robot Policies via Predictive World Modeling

TL;DR

This paper introduces GPC, an inference-time method that enhances pretrained robot policies by integrating a world model for predictive planning, improving performance without retraining across various manipulation tasks.

Contribution

GPC is a novel approach that augments frozen policies with a learned world model for online planning, enabling test-time adaptation in robotic control.

Findings

GPC outperforms standard behavior cloning in multiple tasks.

GPC improves performance in both simulation and real-world experiments.

GPC compares favorably with other inference-time adaptation methods.

Abstract

We present Generative Predictive Control (GPC), an inference-time method for improving pretrained behavior-cloning policies without retraining. GPC augments a frozen diffusion policy at deployment with an action-conditioned world model trained on expert demonstrations and random exploration rollouts. The world model predicts the consequences of action proposals generated by the diffusion policy and enables lightweight online planning that ranks and refines these proposals through model-based look-ahead. By combining a generative prior with predictive foresight, GPC enables test-time adaptation while keeping the original policy fixed. Across diverse robotic manipulation tasks, including state- and vision-based settings in both simulation and real-world experiments, GPC consistently outperforms standard behavior cloning and compares favorably with other inference-time adaptation baselines.