Robotics research has made significant strides in learning, yet mastering basic skills like object placement remains a fundamental challenge. A key bottleneck is the acquisition of large-scale, high-quality data, which is often a manual and laborious process. Inspired by Graspit!, a foundational work that used simulation to automatically generate dexterous grasp poses, we introduce Placeit!, an evolutionary-computation framework for generating valid placement positions for rigid objects. Placeit! is highly versatile, supporting tasks from placing objects on tables to stacking and inserting them. Our experiments show that by leveraging quality-diversity optimization, Placeit! significantly outperforms state-of-the-art methods across all scenarios for generating diverse valid poses. A pick&place pipeline built on our framework achieved a 90% success rate over 120 real-world deployments. This work positions Placeit! as a powerful tool for open-environment pick-and-place tasks and as a valuable engine for generating the data needed to train simulation-based foundation models in robotics.

翻译：机器人学研究在学习方面已取得显著进展，然而掌握物体放置等基本技能仍然是一项根本性挑战。一个关键瓶颈在于获取大规模、高质量数据，这一过程通常是手动且费力的。受Graspit!（一项利用仿真自动生成灵巧抓取姿态的基础性工作）的启发，我们提出了Placeit!，一种用于为刚性物体生成有效放置位置的进化计算框架。Placeit!具有高度通用性，支持从桌面放置到堆叠和插入等多种任务。我们的实验表明，通过利用质量-多样性优化，Placeit!在所有场景下生成多样化有效姿态的性能均显著优于现有先进方法。基于本框架构建的拾放操作流程在120次真实世界部署中取得了90%的成功率。这项工作使Placeit!成为开放环境拾放任务的有力工具，并为生成训练机器人仿真基础模型所需数据提供了宝贵的引擎。