OmniManip:通過以物體為中心的交互基元作為空間約束,邁向通用機器人操作
OmniManip: Towards General Robotic Manipulation via Object-Centric Interaction Primitives as Spatial Constraints
January 7, 2025
作者: Mingjie Pan, Jiyao Zhang, Tianshu Wu, Yinghao Zhao, Wenlong Gao, Hao Dong
cs.AI
摘要
開發能夠在非結構環境中進行操作的通用機器人系統是一個重大挑戰。雖然視覺語言模型(VLM)在高層次的常識推理方面表現出色,但它們缺乏精細的三維空間理解,這是精確操作任務所需的。將VLM在機器人數據集上進行微調,以創建視覺語言行動模型(VLA)是一種潛在的解決方案,但受到高昂的數據收集成本和泛化問題的阻礙。為了應對這些挑戰,我們提出了一種新穎的以物件為中心的表示法,彌合了VLM高層次推理和操作所需的低層次精確性之間的差距。我們的關鍵見解是,物件的規範空間,由其功能性提供,提供了一種結構化和語義上有意義的描述互動基元,如點和方向。這些基元充當橋樑,將VLM的常識推理轉化為可操作的三維空間約束。在這種情況下,我們引入了一個雙閉環、開放詞彙的機器人操作系統:一個循環用於通過基元重採樣、互動渲染和VLM檢查進行高層次規劃,另一個用於通過6D姿態跟踪進行低層次執行。這種設計確保了堅固、實時的控制,而無需進行VLM微調。廣泛的實驗表明,在各種機器人操作任務中具有強大的零樣本泛化能力,突顯了這種方法在自動化大規模模擬數據生成方面的潛力。
English
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.Summary
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