Newest papers

PiHand: Latent Hand-Object Interaction Priors for Physics-Based Policy Learning

Niklas Berndt, Xianghui Xie, Enes Duran, Yixiao Wang, Gerard Pons‐Moll · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31299 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

VR/AR、ゲーム、およびロボット技術にとって、物理的に基盤化されたデータで巧妙な手物相互作用を検出することは重要ですが、微妙な接触ダイナミクス、多様なオブジェクトジオメトリクス、および標的軌道が共に尊重されなければならないため、未だに挑戦的です。既存の方法は、VR/AR、ゲーム、ロボットに限定された転送を伴うモカップ動作の軌道を追跡するか、またはシミュレーションで手だけのモカップ動作を追跡し失敗し、接触忠誠心とサンプル効率を犠牲にしないモカップ生成プレーヤーに依存する。これらの課題に対処するために、我々は、物理的に基盤化された相互作用に気付

Machine translation (Meta M2M100); the English original follows.

Abstract

Synthesizing physically grounded dexterous hand-object interactions is important for VR/AR, gaming, and robotics, yet remains challenging because subtle contact dynamics, diverse object geometries, and target trajectories must be jointly respected while maintaining natural grasps. Existing methods either track mocap trajectories with limited transfer to novel objects, or rely on hand-only generative priors that fail to capture coupled hand-object motion, sacrificing contact fidelity and sample efficiency. To tackle these challenges, we introduce PiHand, a Physically grounded Interaction-aware Hand motion synthesis framework. PiHand first trains expert policies that track mocap demonstrations in simulation, distills them into a unified hand-object interaction prior using a goal-conditioned VAE, and finally solve diverse dexterous manipulation tasks through bounded residual exploration in its latent space. With only a single reward and no complex curriculum training, this yields general tabletop manipulation policies that generalize to unseen objects and trajectories on GRAB and DexYCB. The same prior transfers to object catching and bimanual handover, two tasks that are rarely seen in its training data. Across all tasks, PiHand consistently outperforms baselines in all metrics. We will release the processed data and model checkpoints.

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Topics: Computer science, Artificial intelligence, Object (grammar), Prior probability, Machine learning, Motion (physics)

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