Recent progress in 3D reconstruction has made it easy to create realistic digital twins from everyday environments. However, current digital twins remain largely static and are limited to navigation and view synthesis without embodied interactivity. To bridge this gap, we introduce Dexterous World Model (DWM), a scene-action-conditioned video diffusion framework that models how dexterous human actions induce dynamic changes in static 3D scenes.
Given a static 3D scene rendering and an egocentric hand motion sequence, DWM generates temporally coherent videos depicting plausible human-scene interactions. Our approach conditions video generation on (1) static scene renderings following a specified camera trajectory to ensure spatial consistency, and (2) egocentric hand mesh renderings that encode both geometry and motion cues to model action-conditioned dynamics directly. To train DWM, we construct a hybrid interaction video dataset. Synthetic egocentric interactions provide fully aligned supervision for joint locomotion and manipulation learning, while fixed-camera real-world videos contribute diverse and realistic object dynamics.
Experiments demonstrate that DWM enables realistic and physically plausible interactions, such as grasping, opening, and moving objects, while maintaining camera and scene consistency. This framework represents a first step toward video diffusion-based interactive digital twins and enables embodied simulation from egocentric actions.
Not All Actions Are Equal: Rethinking Conditioning for Dexterous World Model
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Shenzhen University（深圳大学）；Beijing University of Technology（北京工业大学）；JD Explore Academy（京东探索研究院）
University of Washington（华盛顿大学）；Nvidia（英伟达）；Massachusetts Institute of Technology（麻省理工学院）
Teaching a multi-fingered dexterous robot to grasp objects in the real world has been a challenging problem due to its high dimensional state and action space. We propose a robot-learning system that can take a small number of human demonstrations and learn to grasp unseen object poses given partially occluded observations. Our system leverages a small motion capture dataset and generates a large dataset with diverse and successful trajectories for a multi-fingered robot gripper. By adding domain randomization, we show that our dataset provides robust grasping trajectories that can be transfer…
We propose a sim-to-real framework for dexterous manipulation which can generalize to new objects of the same category in the real world. The key of our framework is to train the manipulation policy with point cloud inputs and dexterous hands. We propose two new techniques to enable joint learning on multiple objects and sim-to-real generalization: (i) using imagined hand point clouds as augmented inputs; and (ii) designing novel contact-based rewards. We empirically evaluate our method using an Allegro Hand to grasp novel objects in both simulation and real world. To the best of our knowledge…
UC Berkeley（加州大学伯克利分校）；Meta AI Research（Meta人工智能研究院）；University of Washington（华盛顿大学）
Complex and contact-rich robotic manipulation tasks, particularly those that involve multi-fingered hands and underactuated object manipulation, present a significant challenge to any control method. Methods based on reinforcement learning offer an appealing choice for such settings, as they can enable robots to learn to delicately balance contact forces and dexterously reposition objects without strong modeling assumptions. However, running reinforcement learning on real-world dexterous manipulation systems often requires significant manual engineering. This negates the benefits of autonomous…
METIS: Multi-Source Egocentric Training for Integrated Dexterous Vision-Language-Action Model
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（多媒体信息处理国家重点实验室，计算机学院，北京大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
SparseDFF: Sparse-View Feature Distillation for One-Shot Dexterous Manipulation
CFCS, School of Computer Science, Peking University（北京大学计算机学院CFCS）；Institute for AI, Peking University（北京大学人工智能研究院）；Department of Computer Science, Stanford University（斯坦福大学计算机科学系）；PKU-WUHAN Institute for Artificial Intelligence, China（北京大学武汉人工智能研究院）
