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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8">
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<meta name="description" content="DAGDiff: Guiding Dual-Arm Grasp Diffusion to Stable and Collision-Free Grasps">
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<meta name="keywords" content="dual arm, dual arm grasps, dataset, manipulation">
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<title>DAGDiff</title>
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<div class="container is-max-desktop">
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<div class="columns is-centered">
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<div class="column has-text-centered">
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<h1 class="title is-1 publication-title"><span class="gradient-text">DAGDiff</span>: Guiding Dual-Arm Grasp Diffusion to Stable and Collision-Free Grasps</h1>
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<section class="hero is-light px-5 pb-3" style="background-color: rgb(255, 255, 255);">
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<h2 class="title is-3 mt-3">Abstract</h2>
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Reliable dual-arm grasping is essential for manipulating large and complex objects but remains a challenging problem due to stability, collision, and generalization requirements. Prior methods typically decompose the task into two independent grasp proposals, relying on region priors or heuristics that limit generalization and provide no principled guarantee of stability. We propose DAGDiff, an end-to-end framework that directly denoises to grasp pairs in the $SE(3) \times SE(3)$ space. Our key insight is that stability and collision can be enforced more effectively by guiding the diffusion process with classifier signals, rather than relying on explicit region detection or object priors. To this end, DAGDiff integrates geometry-, stability-, and collision-aware guidance terms that steer the generative process toward grasps that are physically valid and force-closure compliant. We comprehensively evaluate DAGDiff through analytical force-closure checks, collision analysis, and large-scale physics-based simulations, showing consistent improvements over previous work on these metrics. Finally, we demonstrate that our framework generates dual-arm grasps directly from real-world point clouds of previously unseen objects, which are executed on a heterogeneous dual-arm setup where two manipulators reliably grasp and lift them.
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This website is licensed under a <a rel="license"
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href="http://creativecommons.org/licenses/by-sa/4.0/">Creative
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Commons Attribution-Share Alike 4.0 International License</a>. It is borrowing this <a
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href="https://github.com/nerfies/nerfies.github.io">source code</a>
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