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, enhancing robustness and adaptability in cluttered scenes. We characterize MultiGraspNet's performance with an extensive experimental analysis, demonstrating its competitiveness with single-task models on relevant benchmarks. We run real-world experiments on a single-arm multi-gripper robotic setup showing that our approach outperforms the vacuum baseline, grasping 16% percent more seen objects and 32% more of the novel ones, while obtaining competitive results for the parallel task.

翻译：基于视觉的机器人抓取模型实现了对关键、重复且耗力的工业任务的自动化。现有方法通常存在两方面局限：它们要么针对单一夹具，可能应用于昂贵的双臂配置；要么依赖于定制的混合夹具，这些夹具需要特定的学习流程，其逻辑无法跨任务迁移，从而限制了其通用性。本文提出MultiGraspNet，一种新颖的多任务三维深度学习方法，可在统一框架内同时预测平行夹具和真空夹具的可行抓取姿态，使单个机器人能够操控多种末端执行器。该模型在为此目的对齐的、标注丰富的GraspNet-1Billion和SuctionNet-1Billion数据集上进行训练，并生成抓取性掩码以量化场景中每个点对成功抓取的适宜程度。通过共享早期特征并保持夹具特定的细化模块，MultiGraspNet有效利用了不同抓取模式间的互补信息，从而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评估了MultiGraspNet的性能，证明其在相关基准测试中与单任务模型相比具有竞争力。我们在单臂多夹具机器人平台上进行了真实世界实验，结果表明我们的方法优于真空夹具基线，对已知物体的抓取成功率提高了16%，对新物体的抓取成功率提高了32%，同时在平行夹具任务上也取得了有竞争力的结果。