基于单视图观测的真实场景中大型物体的双臂协同灵巧抓取
Real-World Cooperative Bimanual Dexterous Grasp of Large Objects from Single-View Observations
- The University of Auckland（奥克兰大学）
- Chongqing University（重庆大学）
- Southwest University（西南大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文针对机器人双臂抓取大型物体的挑战，提出含多模态数据集、DDPM模块及执行策略的真实场景双臂抓取框架，实验表明其对未见物体抓取成功率高。
中文摘要 AI 辅助
双臂灵巧抓取大型物体是机器人操作领域的关键挑战，但现有多数研究聚焦于顺序操作而非协同抓取，且针对此类双臂任务的方法大多局限于仿真环境。这些限制源于难以获取完整的三维物体模型，以及生成物理上可行的抓取动作。为填补这一空白，本文提出一种真实场景下的双臂抓取框架，包含：采集关节角度、视觉观测和力信号的多模态数据集；基于去噪扩散概率模型（DDPM）的模块，可从分割后的点云生成关节级别的抓取配置；以及将运动规划与在线抓取优化相结合的执行策略，以确保物理稳定性和可行性。该方法能够从单视图输入生成可执行的双臂抓取，减少对完整三维物体模型的依赖，并保证真实场景下的稳定性能。在双机械臂机器人上开展的实验表明，针对不同几何形状和位姿的未见物体，该方法取得了较高的成功率，消融研究也验证了系统各关键组件的贡献。
英文摘要
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.
