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This paper introduces a real-world framework for cooperative bimanual grasping of large objects using single-view observations, addressing the limitations of existing methods that primarily focus on sequential manipulation and simulation. By leveraging a multimodal dataset and a Denoising Diffusion Probabilistic Model (DDPM), the authors generate joint-level grasp configurations from segmented point clouds, enabling effective grasp synthesis without the need for complete 3D object models. Experimental results on a dual-arm robot show high success rates in grasping unseen objects with diverse geometries and poses, validating the framework's practical applicability and robustness.
Achieving high success rates in real-world bimanual grasping from single-view observations could revolutionize robotic manipulation in unstructured environments.
Bimanual dexterous grasping of large objects is a critical challenge in robotic manipulation. However, most existing studies focus on sequential manipulation rather than cooperative grasping, and methods addressing such bimanual tasks have largely been limited to simulation. These limitations stem from the difficulty of acquiring full 3D object models and generating physically plausible grasping actions. To fill this gap, we propose a real-world bimanual grasping framework that includes: a multimodal dataset capturing joint angles, visual observations and force signals; a Denoising Diffusion Probabilistic Model (DDPM)-based module that generates joint-level grasp configurations from segmented point clouds; and an execution strategy that integrates motion planning with online grasp refinement to ensure physical stability and feasibility. Our approach enables the synthesis of executable bimanual grasps from single-view inputs, reducing dependence on complete 3D object models and ensuring stable real-world performance. Experiments on a dual-arm robot demonstrate high success rates across unseen objects with varying geometries and poses, and ablation studies confirm the contributions of key components of our system.
