MultiGraspNet：一种多任务3D视觉模型用于多机械手抓取
MultiGraspNet: A Multitask 3D Vision Model for Multi-gripper Robotic Grasping
AI总结：
MultiGraspNet是一种多任务3D视觉模型，通过统一框架同时预测平行和真空机械手的抓取姿态，提升机器人在复杂环境中的抓取能力和适应性。
摘要：
基于视觉的机器人抓取模型自动化了关键、重复且耗力的工业任务。现有方法通常受到两种限制：要么针对单一机械手，可能应用于成本高昂的双臂系统，要么依赖于定制的混合机械手，需要定制化的学习过程，其逻辑无法跨任务转移，限制了其通用性。在本工作中，我们提出了MultiGraspNet，一种新颖的多任务3D深度学习方法，能够在统一框架中同时预测平行机械手和真空机械手的可行姿态，使单个机器人能够处理多个末端执行器。该模型在丰富的标注数据集GraspNet-1Billion和SuctionNet-1Billion上进行训练，这两个数据集已针对此目的对齐，并生成抓取可能性掩码，量化每个场景点对成功抓取的适合性。通过共享早期阶段的特征同时保持机械手特定的细化器，MultiGraspNet有效利用了不同抓取模态的互补信息，增强了在杂乱场景中的鲁棒性和适应性。我们通过广泛的实验分析来表征MultiGraspNet的性能，证明其在相关基准上与单任务模型具有竞争力。我们在单臂多机械手机器人系统上进行了真实世界实验，结果显示我们的方法在真空基线基础上表现更优，能够抓取16%更多已见物体和32%更多新物体，同时在平行任务上获得具有竞争力的结果。
英文摘要：
Vision-based models for robotic grasping automate critical, repetitive, and draining industrial tasks. Existing approaches are typically limited in two ways: they either target a single gripper and are potentially applied on costly dual-arm setups, or rely on custom hybrid grippers that require ad-hoc learning procedures with logic that cannot be transferred across tasks, restricting their general applicability. In this work, we present MultiGraspNet, a novel multitask 3D deep learning method that predicts feasible poses simultaneously for parallel and vacuum grippers within a unified framework, enabling a single robot to handle multiple end effectors. The model is trained on the richly annotated GraspNet-1Billion and SuctionNet-1Billion datasets, which have been aligned for the purpose, and generates graspability masks quantifying the suitability of each scene point for successful grasps. By sharing early-stage features while maintaining gripper-specific refiners, MultiGraspNet effectively leverages complementary information across grasping modalities. This design preserves a compact architectural footprint of only 15.75M parameters and enables fast inference on a single GPU, enhancing adaptability and efficiency in cluttered scenes. We characterize MultiGraspnet's performance with an extensive experimental analysis, demonstrating its competitiveness with single-task models on relevant benchmarks while reducing computational cost. Moreover, real-world experiments on a single-arm multi-gripper robotic setup show that our approach outperforms normalization-based multi-gripper approaches. Project page: https://vandal-lab.github.io/multigraspnet-project
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
