DexMove: Learning Tactile-Guided Non-Prehensile Manipulation with Dexterous Hands

Non-prehensile manipulation offers a robust alternative to traditional pick-andplace methods for object repositioning. However, learning such skills with dexterous, multi-fingered hands remains largely unexplored, leaving their potential for stable and efficient manipulation underutilized. Progress has been limited by the lack of large-scale, contact-aware non-prehensile datasets for dexterous hands and the absence of wrist-finger control policies. To bridge these gaps, we present DexMove, a tactile-guided non-prehensile manipulation framework for dexterous hands. DexMove combines a scalable simulation pipeline that generates physically plausible wrist-finger trajectories with a wearable device, which captures multi-finger contact data from human demonstrations using vision-based tactile sensors. Using these data, we train a flow-based policy that enables realtime, synergistic wrist-finger control for robust non-prehensile manipulation of diverse tabletop objects. In real-world experiments, DexMove successfully manipulated six objects of varying shapes and materials, achieving a 77.8% success rate. Our method outperforms ablated baselines by 36.6% and improves efficiency by nearly 300%. Furthermore, the learned policy generalizes to languageconditioned, long-horizon tasks such as object sorting and desktop tidying. Project page: https://peilin-666.github.io/projects/DexMove/ Figure 1: Overview of DexMove. The framework integrates synthetic non-prehensile manipulation trajectories and human-demonstrated tactile data to train a flow-matching policy for dexterous hands. The learned policy generalizes across diverse objects, surface frictions, and various language-conditioned tasks such as tidying.