PolaRiS: Scalable Real-to-Sim Evaluations for Generalist Robot Policies

TL;DR

PolaRiS introduces a scalable, high-fidelity simulation framework that converts real-world scenes into interactive environments, enabling more accurate and efficient evaluation of generalist robot policies across diverse scenarios.

Contribution

The paper presents PolaRiS, a novel real-to-sim evaluation framework using neural reconstruction and co-training to improve simulation fidelity and correlation with real-world robot policy performance.

Findings

PolaRiS evaluations correlate better with real-world performance than existing benchmarks.

The framework enables rapid creation of diverse simulation environments.

PolaRiS supports zero-shot evaluation in unseen environments.

Abstract

A significant challenge for robot learning research is our ability to accurately measure and compare the performance of robot policies. Benchmarking in robotics is historically challenging due to the stochasticity, reproducibility, and time-consuming nature of real-world rollouts. This challenge is exacerbated for recent generalist policies, which has to be evaluated across a wide variety of scenes and tasks. Evaluation in simulation offers a scalable complement to real world evaluations, but the visual and physical domain gap between existing simulation benchmarks and the real world has made them an unreliable signal for policy improvement. Furthermore, building realistic and diverse simulated environments has traditionally required significant human effort and expertise. To bridge the gap, we introduce Policy Evaluation and Environment Reconstruction in Simulation (PolaRiS), a scalable real-to-sim framework for high-fidelity simulated robot evaluation. PolaRiS utilizes neural reconstruction methods to turn short video scans of real-world scenes into interactive simulation environments. Additionally, we develop a simple simulation data co-training recipe that bridges remaining real-to-sim gaps and enables zero-shot evaluation in unseen simulation environments. Through extensive paired evaluations between simulation and the real world, we demonstrate that PolaRiS evaluations provide a much stronger correlation to real world generalist policy performance than existing simulated benchmarks. Its simplicity also enables rapid creation of diverse simulated environments. As such, this work takes a step towards distributed and democratized evaluation for the next generation of robotic foundation models.