Title: DexNDM: Closing the Reality Gap for Dexterous In-Hand Rotation via Joint-Wise Neural Dynamics Model
Primary area: applications to robotics, autonomy, planning
Keywords: In-Hand Object Rotation; Sim-to-Real; Neural Dynamics Model

Abstract: Achieving generalized in-hand object rotation remains a significant challenge in robotics, largely due to the difficulty of transferring policies from simulation to the real world. The complex, contact-rich dynamics of dexterous manipulation create a "reality gap" that has limited prior work to constrained scenarios involving simple geometries, limited object sizes and aspect ratios, constrained wrist poses, or customized hands. We address this sim-to-real challenge with a novel framework that enables a single policy, trained in simulation, to generalize to a wide variety of objects and conditions in the real world. The core of our method is a joint-wise dynamics model that learns to bridge the reality gap by effectively fitting limited amount of real-world collected data and then adapting the sim policy’s actions accordingly.  The model is highly data‑efficient and generalizable across different whole‑hand interaction distributions by factorizing dynamics across joints, compressing system-wide influences into low‑dimensional variables, and learning each joint’s evolution from its own dynamic profile, implicitly capturing these net effects. We pair this with a fully autonomous data collection strategy that gathers diverse, real-world interaction data with minimal human intervention. Our complete pipeline demonstrates unprecedented generality: a single policy successfully rotates challenging objects with complex shapes (*e.g.*, animals), high aspect ratios (up to 5.33), and small sizes, all while handling diverse wrist orientations and rotation axes. Comprehensive real-world evaluations and a teleoperation application for complex tasks validate the effectiveness and robustness of our approach. Website: [DexNDM](https://projectwebsitex.github.io/neudyn-reorientation/).

TL;DR: A sim-to-real approach that enables unprecedented in-hand rotation (long, small, complex shapes; down-facing hand) in the real world.