Pick-and-place is an important manipulation task in domestic or manufacturing applications. There exist many works focusing on grasp detection with high picking success rate but lacking consideration of downstream manipulation tasks (e.g., placing). Although some research works proposed methods to incorporate task conditions into grasp selection, most of them are data-driven and are therefore hard to adapt to arbitrary operating environments. Observing this challenge, we propose a general task-oriented pick-place framework that treats the target task and operating environment as placing constraints into grasping optimization. Combined with existing grasp detectors, our framework is able to generate feasible grasps for different downstream tasks and adapt to environmental changes without time-consuming re-training processes. Moreover, the framework can accept different definitions of placing constraints, so it is easy to integrate with other modules. Experiments in the simulator and real-world on multiple pick-place tasks are conducted to evaluate the performance of our framework. The result shows that our framework achieves a high and robust task success rate on a wide variety of the pick-place tasks.

翻译：抓取-放置是家庭或制造应用中的重要操作任务。现有许多研究聚焦于高成功率的抓取检测，但缺乏对下游操作任务（如放置）的考虑。尽管部分研究提出了将任务条件纳入抓取选择的方法，但这些方法大多依赖数据驱动，难以适应任意操作环境。针对这一挑战，我们提出了一种通用任务导向的抓取-放置框架，将目标任务与操作环境视为放置约束，并将其融入抓取优化过程。结合现有抓取检测器，该框架能够为不同下游任务生成可行抓取方案，并适应环境变化而无需耗时的重新训练。此外，该框架可接受不同定义的放置约束，便于与其他模块集成。通过在仿真器与真实环境中对多种抓取-放置任务的实验评估，结果表明本框架在广泛任务中实现了高且鲁棒的任务成功率。