DexNDM: Closing the Reality Gap for Dexterous In-Hand Rotation via
  Joint-Wise Neural Dynamics Model
Authors: name: Xueyi Liu, name: He Wang, name: Li Yi

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: https://meowuu7.github.io/DexNDM/