[2603.05504] RoboPocket: Improve Robot Policies Instantly with Your Phone

Computer Science > Robotics arXiv:2603.05504 (cs) [Submitted on 5 Mar 2026] Title:RoboPocket: Improve Robot Policies Instantly with Your Phone Authors:Junjie Fang, Wendi Chen, Han Xue, Fangyuan Zhou, Tian Le, Yi Wang, Yuting Zhang, Jun Lv, Chuan Wen, Cewu Lu View a PDF of the paper titled RoboPocket: Improve Robot Policies Instantly with Your Phone, by Junjie Fang and 9 other authors View PDF HTML (experimental) Abstract:Scaling imitation learning is fundamentally constrained by the efficiency of data collection. While handheld interfaces have emerged as a scalable solution for in-the-wild data acquisition, they predominantly operate in an open-loop manner: operators blindly collect demonstrations without knowing the underlying policy's weaknesses, leading to inefficient coverage of critical state distributions. Conversely, interactive methods like DAgger effectively address covariate shift but rely on physical robot execution, which is costly and difficult to scale. To reconcile this trade-off, we introduce RoboPocket, a portable system that enables Robot-Free Instant Policy Iteration using single consumer smartphones. Its core innovation is a Remote Inference framework that visualizes the policy's predicted trajectory via Augmented Reality (AR) Visual Foresight. This immersive feedback allows collectors to proactively identify potential failures and focus data collection on the policy's weak regions without requiring a physical robot. Furthermore, we implement an asynchr...