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Learning contact representations in real-world clutter for universal robotic grasping
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DOI：10.1038/s42256-026-01292-y.png)
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Abstract
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A long-standing goal in robotics is to create general-purpose agents that can learn foundational skills applicable across diverse hardware and environments. In manipulation, this is hindered by a fundamental mismatch: deep learning policies are data-efficient but hardware-specific, whereas analytical planners are hardware-agnostic but fail under perceptual uncertainty. Here we introduce SpaHybGen, a framework that unifies their strengths through learned, universal contact representations. We first train a neural network to infer spatial contact features—a hardware-agnostic representation of potential grasp points—directly from noisy depth observations. These features then guide a differentiable optimizer that computes stable grasps for articulated hand models. This hybrid design enables zero-shot generalization: our system, trained once, successfully empowered seven distinct robotic hands (from two to five fingers) without any hardware-specific retraining, achieving grasping success rates of 94.3%–98.0% in semi-cluttered scenes. It further enabled dynamic grasping at 20 Hz in dense clutter and multi-hand coordination for complex tasks. By decoupling perception from action through a shared contact interface, we provide a pathway towards reusable and adaptable manipulation intelligence, a key step for general-purpose robotics. Our code and models are open-sourced to support this vision. Wang et al. design efficient robot–environment interaction representations that achieve generalization across articulated robotic hand models and task adaptability in diverse cluttered grasping scenarios, suggesting a path towards general-purpose robotics.
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