ExoGS: A 4D Real-to-Sim-to-Real Framework for Scalable Manipulation Data Collection
Yiming Wang, Ruogu Zhang, Minyang Li, Hao Shi, Junbo Wang, Deyi Li, Jieji Ren, Wenhai Liu, Weiming Wang, Hao-Shu Fang

TL;DR
ExoGS introduces a novel 4D Real-to-Sim-to-Real framework utilizing a passive exoskeleton and 3D Gaussian Splatting for scalable, accurate manipulation data collection and improved policy generalization in robotics.
Contribution
It presents a robot-free, 4D real-to-sim-to-real framework with a passive exoskeleton and semantic augmentation for scalable manipulation data collection.
Findings
Significantly improves data efficiency in manipulation tasks.
Enhances policy robustness under visual domain shifts.
Enables geometry-consistent replay and large-scale data augmentation.
Abstract
Real-to-Sim-to-Real technique is gaining increasing interest for robotic manipulation, as it can generate scalable data in simulation while having narrower sim-to-real gap. However, previous methods mainly focused on environment-level visual real-to-sim transfer, ignoring the transfer of interactions, which could be challenging and inefficient to obtain purely in simulation especially for contact-rich tasks. We propose ExoGS, a robot-free 4D Real-to-Sim-to-Real framework that captures both static environments and dynamic interactions in the real world and transfers them seamlessly to a simulated environment. It provides a new solution for scalable manipulation data collection and policy learning. ExoGS employs a self-designed robot-isomorphic passive exoskeleton AirExo-3 to capture kinematically consistent trajectories with millimeter-level accuracy and synchronized RGB observations…
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Taxonomy
TopicsRobot Manipulation and Learning · Teleoperation and Haptic Systems · Prosthetics and Rehabilitation Robotics
