ZeroDex: Zero-Shot Long-Horizon Dexterous Manipulation via Multi-View 3D-Grounded VLM Reasoning

We present ZeroDex, a zero-shot framework for long-horizon dexterous manipulation that grounds language instructions into executable 3D task plans from calibrated multi-view RGB images. Our framework builds on the grounding ability that vision-language models (VLMs) already exhibit in 2D and lifts their predictions into 3D through multi-view fusion, which extends VLM grounding beyond image-space keypoints to the metric 3D quantities that manipulation actually requires: contact points, placement targets, and tool-motion anchors. Because execution is driven by these 3D groundings rather than a learned policy, the system preserves the VLM's open-world generalization, without collecting demonstrations or post-training for a specific task or embodiment. The VLM decomposes a complex instruction into a sequence of atomic tasks, each executed by a primitive from a small reusable library that covers manipulation tasks including pick-and-place and tool-use: for tool-use, we retrieve an object-centric atomic action corresponding to the inferred skill category and align its stored 6D tool trajectory to the grounded keypoints; for dexterous execution, we expand the lifted grasp keypoint into a task-conditioned grasp affordance region and generate feasible grasp-motion pairs with an arm-hand motion generator. Real-world experiments show improved 3D grounding accuracy and higher task success than zero-shot visual-prompting and constraint-based baselines, both of which ground from a single RGB-D view, and our demonstration-free system outperforms VLA policies fine-tuned on 100 task demonstrations. We further demonstrate long-horizon manipulation, where execution verification and replanning recover from intermediate failures, and show that the system generalizes to unseen objects and tool-use tasks in novel scenes.