OmniManip: Towards General Robotic Manipulation via Object-Centric Interaction Primitives as Spatial Constraints (2501.03841v1)

Abstract: The development of general robotic systems capable of manipulating in unstructured environments is a significant challenge. While Vision-LLMs(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.

• The paper introduces OmniManip, a dual closed-loop system using object-centric interaction primitives to enable general manipulation by bridging semantic reasoning and low-level control without costly VLM fine-tuning.
• OmniManip translates high-level VLM understanding into actionable 3D spatial constraints by defining interaction primitives in an object's canonical space, aligned with its functional affordances.
• Experiments show OmniManip achieves superior zero-shot generalization and higher success rates than previous methods in diverse rigid and articulated object manipulation tasks.

A Critical Review of "OmniManip: Towards General Robotic Manipulation via Object-Centric Interaction Primitives as Spatial Constraints"

The paper "OmniManip: Towards General Robotic Manipulation via Object-Centric Interaction Primitives as Spatial Constraints" addresses a significant challenge in robotics — achieving general manipulation capabilities in unstructured environments. The authors propose a novel approach leveraging object-centric interaction primitives to bridge the gap between high-level semantic reasoning and precise, low-level robotic manipulation, without the need for extensive fine-tuning of Vision-LLMs (VLM).

Overview and Methodology

The central innovation of the paper lies in the introduction of OmniManip, a dual closed-loop system that integrates high-level planning and low-level execution using object-centric interaction primitives. These primitives consist of interaction points and directions defined in an object's canonical space, which are aligned with the object's functional affordances. This representation provides a semantically meaningful and geometrically precise framework for robotic manipulation tasks.

OmniManip is characterized by two primary loops: one for high-level planning using primitive resampling and interaction rendering to enhance the robustness of the VLM's commonsense reasoning, and another for low-level execution through 6D pose tracking. This structure ensures both effective decision-making and real-time control, significantly improving zero-shot generalization across various manipulation tasks.

Technical Contributions and Results

The paper’s contributions can be synthesized as follows:

1. Object-Centric Interaction Representation: By structuring interaction primitives within an object’s canonical space, the authors effectively translate VLM's high-level semantic reasoning into actionable 3D spatial constraints.
2. Dual Closed-Loop System: The integration of closed-loop planning and execution without VLM fine-tuning represents a noteworthy advancement. The self-correcting mechanism based on rendering, resampling, and checking minimizes the risk of failure due to VLM hallucinations, improving the robustness of manipulation.
3. Zero-Shot Generalization: Extensive experiments demonstrate the approach’s ability to generalize without specific training data, highlighting OmniManip’s potential for scalable robotic manipulation.

The experimental results show that OmniManip outperforms existing methods, such as VoxPoser, CoPa, and ReKep, by a notable margin in both rigid and articulated object manipulation tasks. Specifically, the dual closed-loop enhances task success rates significantly, demonstrating superior real-time adaptability and execution reliability.

Theoretical and Practical Implications

The proposal of OmniManip marks a step forward in the utilization of foundational AI models for robotics. The introduction of object-centric interaction primitives effectively obviates the need for costly VLM fine-tuning by leveraging the semantic understanding these models provide. This not only broadens the applicability of VLMs in robotic contexts but also suggests a pathway towards the development of more autonomous, flexible robotic systems capable of operating efficiently in unstructured environments.

Practically, OmniManip demonstrates potential for automating the generation of robot manipulation data, thus contributing to more scalable and efficient approaches in imitation learning of robotic systems. This automation could mitigate the traditionally high data collection costs associated with training such models, facilitating broader adoption and further advancements in robotics.

Future Directions and Challenges

While promising, the approach introduced in this paper is not without limitations. The focus on static, rigid, and articulated objects suggests an area for future exploration in handling deformable objects, which remains a complex challenge in robotic manipulation.

Furthermore, the reliance on object-centric representations necessitates high-quality 3D mesh generation, which remains a technical bottleneck. Addressing these challenges through advancements in 3D reconstruction techniques and expanding the capability to include more diverse object types will be crucial for future research.

Overall, "OmniManip: Towards General Robotic Manipulation via Object-Centric Interaction Primitives as Spatial Constraints" provides a compelling framework for advancing robotic manipulation capabilities, offering a foundation for future exploration and development in this dynamic and rapidly evolving field.