[2510.23571] RobotArena $\infty$: Scalable Robot Benchmarking via Real-to-Sim Translation

Computer Science > Robotics arXiv:2510.23571 (cs) [Submitted on 27 Oct 2025 (v1), last revised 20 Mar 2026 (this version, v3)] Title:RobotArena $\infty$: Scalable Robot Benchmarking via Real-to-Sim Translation Authors:Yash Jangir, Yidi Zhang, Pang-Chi Lo, Kashu Yamazaki, Chenyu Zhang, Kuan-Hsun Tu, Tsung-Wei Ke, Lei Ke, Yonatan Bisk, Katerina Fragkiadaki View a PDF of the paper titled RobotArena $\infty$: Scalable Robot Benchmarking via Real-to-Sim Translation, by Yash Jangir and 9 other authors View PDF Abstract:The pursuit of robot generalists, agents capable of performing diverse tasks across diverse environments, demands rigorous and scalable evaluation. Yet real-world testing of robot policies remains fundamentally constrained: it is labor-intensive, slow, unsafe at scale, and difficult to reproduce. As policies expand in scope and complexity, these barriers only intensify, since defining "success" in robotics often hinges on nuanced human judgments of execution quality. We introduce RobotArena Infinity, a new benchmarking framework that overcomes these challenges by shifting vision-language-action (VLA) evaluation into large-scale simulated environments augmented with online human feedback. Leveraging advances in vision-language models, 2D-to-3D generative modeling, and differentiable rendering, our approach automatically converts video demonstrations from widely used robot datasets into simulated counterparts. Within these digital twins, we assess VLA policies using...