OmniManip - A general robot operation framework jointly developed by Zhiyuan Robotics and Peking University
OmniManip is a general-purpose robot manipulation framework developed by the joint laboratory of Peking University and Zhiyuan Robotics. By combining the high-level reasoning capabilities of a visual language model (VLM) with precise 3D manipulation capabilities, it enables robots to perform non-linear maneuvers...
What is OmniManip?
OmniManip is a general-purpose robot manipulation framework developed by the joint laboratory of Peking University and Zhiyuan Robotics. By combining the high-level reasoning capabilities of the Visual Language Model (VLM) with precise 3D manipulation capabilities, it enables robots to perform general operations in unstructured environments. The core of the framework lies in an object-centered interactive primitive representation method. By decomposing tasks into multiple structured stages, it transforms natural language instructions into executable 3D spatial constraints based on the common-sense reasoning capabilities provided by the VLM.
OmniManip's main functions
- Zero-shot generalization abilityOmniManip can handle a wide variety of open-vocabulary instructions and objects, and can achieve strong generalization capabilities in a variety of robotic operation tasks without training.
- Cross-robot form capabilityOmniManip is a hardware-independent approach that can be easily deployed on different types of robotic platforms, such as dual-arm humanoid robots.
- Large-scale simulation data generationOmniManip is designed to automatically generate large-scale robot operation simulation data, providing strong data support for future research.
OmniManip's technical principles
- Object-centered interaction primitive representationOmniManip proposes an object-centric representation method that defines interaction primitives (such as interaction points and directions) through the object's canonical space, transforming the output of VLM into executable 3D spatial constraints. These interaction primitives are defined in the object's standard space, ensuring consistency across different scenarios and enabling more general and reusable operation strategies.
- Dual closed-loop system designOmniManip employs a dual closed-loop system, one for high-level planning and the other for low-level execution.
- Closed-loop planningOmniManip implements closed-loop reasoning for VLM through interactive rendering and interactive primitive resampling. This mechanism can detect and correct errors in VLM reasoning (such as the illusion problem), ensuring the accuracy of the planning results.
- Closed-loop executionDuring the execution phase, OmniManip uses a 6D pose tracker to update the object's pose in real time and convert it into the operating trajectory of the robotic arm's end effector, thereby achieving robust real-time control.
- Task decomposition and spatial constraintsOmniManip breaks down complex tasks into multiple stages, each with spatial constraints defined by interaction primitives. For example, in the task of "pouring tea into a cup," the system breaks it down into two stages: "grabbing the teapot" and "pouring the tea," generating corresponding interaction points and directions for each stage.
OmniManip's project address
- Project official website:https://omnimanip.github.io/
- GitHub repository:https://github.com/pmj110119/OmniManip
- arXiv technical paper:https://arxiv.org/pdf/2501.03841
Application scenarios of OmniManip
- Handling items in daily lifeSuch as pouring tea, arranging flowers, tidying up the table, etc.
- Industrial Automation: Achieve automation of complex tasks through precise three-dimensional manipulation capabilities.
- service robotsPerforming tasks in unstructured environments, such as home services or medical assistance.