Kanav Arora — accepted AI-conference papers

2026

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

Arhan Jain, Mingtong Zhang, Kanav Arora, William Chen, Marcel Torne, Muhammad Zubair Irshad +8

RSS 2026poster

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 …

Cited by 26 Source ↗Scholar ↗

2025

RoboArena: Distributed Real-World Evaluation of Generalist Robot Policies

Pranav Atreya, Karl Pertsch, Tony Lee, Moo Jin Kim, Arhan Jain, Artur Kuramshin +20

CoRL 2025oral

Comprehensive, unbiased, and comparable evaluation of modern generalist policies is uniquely challenging: existing approaches for robot benchmarking typically rely on heavy standardization, either by specifying fixed evaluation tasks and environments, or by hosting centralized "robot challenges", an…

Cited by 0 Source ↗Scholar ↗