analiza aiqbench
RoboPocket addresses a critical bottleneck in robot learning by enabling scalable, robot-free policy iteration through smartphone-based AR guidance and cloud-side inference. This system allows users to collect targeted corrective data without deploying physical robots, significantly improving sample efficiency for real-world manipulation tasks. It represents a major step forward in making advanced robotic policies accessible and trainable outside of specialized labs.
RoboPocket: Improve Robot Policies Instantly with Your Phone
arXiv:2603.05504v3 Announce Type: replace-cross Abstract: Scaling robot learning is bottlenecked not only by data volume, but also by whether the data targets the policy's failures. Existing robot-free handheld interfaces enable portable in-the-wild data collection, yet remain open-loop: users record demonstrations without feedback from the current policy. Interactive methods such as DAgger provide such feedback by collecting corrective on-policy data, but require deploying imperfect policies on physical robots, making them costly, unsafe, and difficult to scale. We present RoboPocket, a smartphone-based system for robot-free policy iteration. By combining on-device sensing, display, and communication with cloud-side policy inference, RoboPocket provides real-time collection guidance and visualizes the policy's predicted trajectory in augmented reality as AR Visual Foresight. This allows users to identify policy weaknesses and collect targeted corrective data without deploying a physical robot. An asynchronous finetuning pipeline continuously incorporates new corrections and updates the policy within minutes, realizing a DAgger-inspired robot-free correction loop. Across diverse real-world manipulation tasks, we validate RoboPocket as a scalable data engine and show that online corrective data collection improves sample efficiency by up to $2\times$ over scaling offline demonstrations. Project page and videos: https://robo-pocket.github.io.
Abstract:Scaling robot learning is bottlenecked not only by data volume, but also by whether the data targets the policy's failures. Existing robot-free handheld interfaces enable portable in-the-wild data collection, yet remain open-loop: users record demonstrations without feedback from the current policy. Interactive methods such as DAgger provide such feedback by collecting corrective on-policy data, but require deploying imperfect policies on physical robots, making them costly, unsafe, and difficult to scale. We present RoboPocket, a smartphone-based system for robot-free policy iteration. By combining on-device sensing, display, and communication with cloud-side policy inference, RoboPocket provides real-time collection guidance and visualizes the policy's predicted trajectory in augmented reality as AR Visual Foresight. This allows users to identify policy weaknesses and collect targeted corrective data without deploying a physical robot. An asynchronous finetuning pipeline continuously incorporates new corrections and updates the policy within minutes, realizing a DAgger-inspired robot-free correction loop. Across diverse real-world manipulation tasks, we validate RoboPocket as a scalable data engine and show that online corrective data collection improves sample efficiency by up to $2\times$ over scaling offline demonstrations. Project page and videos: this https URL.
Accepted to CoRL 2026. Project page: this https URL
Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2603.05504 [cs.RO]
(or arXiv:2603.05504v3 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2603.05504 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 290 cuvinte