OmniManip: Towards General Robotic Manipulation via Object-Centric
  Interaction Primitives as Spatial Constraints
Authors: name: Mingjie Pan, name: Jiyao Zhang, name: Tianshu Wu, name: Yinghao Zhao, name: Wenlong Gao, name: Hao Dong

The development of general robotic systems capable of manipulating in
unstructured environments is a significant challenge. While Vision-Language
Models(VLM) excel in high-level commonsense reasoning, they lack the
fine-grained 3D spatial understanding required for precise manipulation tasks.
Fine-tuning VLM on robotic datasets to create Vision-Language-Action
Models(VLA) is a potential solution, but it is hindered by high data collection
costs and generalization issues. To address these challenges, we propose a
novel object-centric representation that bridges the gap between VLM's
high-level reasoning and the low-level precision required for manipulation. Our
key insight is that an object's canonical space, defined by its functional
affordances, provides a structured and semantically meaningful way to describe
interaction primitives, such as points and directions. These primitives act as
a bridge, translating VLM's commonsense reasoning into actionable 3D spatial
constraints. In this context, we introduce a dual closed-loop, open-vocabulary
robotic manipulation system: one loop for high-level planning through primitive
resampling, interaction rendering and VLM checking, and another for low-level
execution via 6D pose tracking. This design ensures robust, real-time control
without requiring VLM fine-tuning. Extensive experiments demonstrate strong
zero-shot generalization across diverse robotic manipulation tasks,
highlighting the potential of this approach for automating large-scale
simulation data generation.