Source-linked AI summary
OmniManip: Towards General Robotic Manipulation via Object-Centric Interaction Primitives as Spatial Constraints
Mingjie Pan, Jiyao Zhang, Tianshu Wu, Yinghao Zhao, Wenlong Gao, Hao Dong
TL;DR
General robotic manipulation requires precise 3D spatial understanding that VLMs do not provide, while VLA fine-tuning is costly and can limit generalization. OmniManip uses canonical object-centric interaction primitives and dual closed-loop planning and execution, achieving strong zero-shot generalization across diverse tasks without VLM fine-tuning.
Problem
VLMs lack the fine-grained 3D spatial understanding needed for precise manipulation, while VLM-to-VLA fine-tuning requires costly data and can produce agent-specific representations.
Method
OmniManip represents interaction points and directions in object canonical space and uses dual closed loops for primitive-based planning and 6D pose-tracked execution.
Results
The method demonstrates strong zero-shot generalization across diverse manipulation tasks without VLM fine-tuning.
Takeaways & Limitations
The approach provides a foundation for scalable open-vocabulary manipulation and automated robotic data generation.
Abstract
from arXiv · showhide
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.
1. Introduction
OmniManip addresses the gap between VLM commonsense reasoning and the fine-grained 3D understanding needed for manipulation by representing object interactions in canonical space. Its dual closed-loop system supports both planning correction and pose-tracked execution without VLM fine-tuning.
- VLMs provide high-level semantic reasoning but lack the fine-grained 3D spatial understanding required for precise manipulation in unstructured environments.
- Fine-tuning VLMs into VLAs requires costly, time-consuming robotic data and produces agent-specific representations with limited generalizability.
- OmniManip represents interaction points and directions in an object’s affordance-defined canonical space, translating semantic reasoning into structured 3D spatial constraints.
- The method combines universal 6D pose estimation, generated object meshes, axis-based direction sampling, VLM-predicted points, and VLM-selected task-relevant primitives.
- A dual closed-loop system uses rendering and primitive resampling for reasoning, while pose tracking closes the execution loop.
- Experiments report strong zero-shot generalization across diverse manipulation tasks and potential for automating robotic manipulation data generation without VLM fine-tuning.
2. Related Work
Prior work applies foundation models, keypoints, and 6D poses to robotic manipulation, but each representation has limitations in data requirements, stability, geometric detail, or object variation. OmniManip combines canonical functional points and directions to retain fine-grained geometry while improving representation stability.
- Foundation-model robotics approaches use VLM commonsense reasoning for environment understanding and high-level control, while VLA methods fine-tune VLMs on robotics datasets.
- Keypoint-based methods facilitate automatic high-level planning but can be unstable under occlusion and difficult to extract or select reliably.
- 6D-pose methods provide robust long-range object relationships and occlusion tolerance but require prior geometric modeling and lack fine-grained geometry.
- OmniManip combines keypoint-level geometric detail with 6D-pose stability by extracting functional points and directions in object canonical coordinates.
3. Method
OmniManip formulates manipulation as object-centric interaction primitives and spatial constraints, then combines VLM-based planning feedback with pose-tracked execution for closed-loop control.
- Task formulation: Tasks are decomposed into stages whose active and passive objects interact through spatially constrained primitives.Each stage specifies an action, participating objects, and constraints governing their interaction.
- Object-centric representation: Each interaction primitive contains a canonical-space interaction point and direction that encode functional and geometric properties consistently across scenarios.The point marks where interaction occurs, while the direction represents the task-relevant primary axis.
- Primitive extraction: The system obtains object meshes and canonical poses, samples candidate directions, and uses a VLM to generate task-relevant interaction primitives and constraints.Object canonicalization uses pose estimation, while single-view 3D generation supplies detailed meshes.
- Closed-loop planning: RRC closes the planning loop by rendering candidate interactions, asking the VLM to validate them, and refining directions when alignment is inadequate.The process evaluates success, failure, or refinement outcomes and samples refined directions around the predicted direction.
- Closed-loop execution: Execution minimizes constraint, collision, and path losses to select an end-effector pose while satisfying spatial relationships, avoiding obstacles, and maintaining smooth motion.Constraint loss measures deviation from desired active-passive relationships; collision and path losses enforce safety and smoothness.
- Closed-loop execution: Real-time 6D tracking updates active and passive object poses, allowing the end-effector target to adapt during dynamic execution.This feedback addresses disturbances such as grasp-induced object motion or target displacement.
4. Experiment
OmniManip is evaluated on diverse open-vocabulary manipulation tasks, with experiments examining generalization, primitive reliability and efficiency, closed-loop planning and execution, and demonstration generation. Results indicate robust zero-shot performance, viewpoint-stable primitives, improved sampling efficiency, and benefits from dual closed-loop control.
- Open-Vocabulary Manipulation: OmniManip was evaluated on 12 open-vocabulary manipulation tasks without task-specific training and showed robust zero-shot generalization across simple, directional, and articulated-object tasks.The evaluation used 10 trials per approach with reconfigured object layouts between trials.
- Primitive Stability: OmniManip’s canonical-space primitives provide more stable, task-relevant interaction points than surface-based or texture-sensitive alternatives.The method aligns sampling with object functionality, while ReKep and CoPa are described as sensitive to spatial geometry, image texture, or part shape.
- Viewpoint Consistency: OmniManip’s performance is nearly invariant across viewpoints, whereas ReKep’s performance is significantly affected by viewpoint changes.The comparison uses the “Recycle the battery” task as a case study.
- Sampling Efficiency: Canonical-axis sampling requires fewer iterations and achieves higher task success than uniform sampling in SO(3).The comparison is reported in Table 3 and attributes the efficiency to alignment between the canonical space and object functionality.
- Closed-Loop Planning: Disabling closed-loop planning decreases task success by over 15% for both rigid and articulated object manipulation tasks.OmniManip uses Resampling, Rendering, and Checking to detect and correct planning errors before execution.
- Closed-Loop Execution: Object-centric 6D pose tracking supports closed-loop execution under changing relative poses and moving targets, including when interaction primitives become occluded.The method continues tracking canonical-space primitives from the object pose even when the primitives are no longer visible.
- Demonstration Generation: OmniManip generated zero-shot demonstration trajectories without task-specific details or prior object knowledge, and 150 trajectories per task were used to train behavior-cloning policies.The resulting policies achieved high success rates, as reported in Table 4.
5. Conclusion
OmniManip uses object-canonical interaction primitives to translate VLM semantics into actionable 3D constraints, with dual closed-loop planning and execution without VLM fine-tuning. It shows strong zero-shot generalization across manipulation tasks, while remaining limited by deformable objects, mesh quality, and computational cost.
- OmniManip structures interaction primitives in object canonical space to translate high-level VLM reasoning into actionable 3D spatial constraints.
- Its dual closed-loop system supports robust decision-making and execution without VLM fine-tuning.
- The approach demonstrates strong zero-shot generalization across a variety of manipulation tasks.
- Limitations: OmniManip cannot model deformable objects, depends on 3D AIGC mesh quality, and faces computational challenges from multiple VLM calls.