用进化算法自动生成物体放置位置，解决机器人抓放数据难问题。
Placeit! A Framework for Learning Robot Object Placement Skills
- 基于质量-多样性优化的进化计算框架，自动生成有效放置位姿。
- 在120次真实部署中实现90%成功率，优于现有方法。
- 适合需要大量放置数据的机器人训练与开放环境任务。
机器人研究在学习能力上取得显著进展，但掌握如物体放置等基础技能仍是核心挑战。主要瓶颈在于获取大规模高质量数据，而这一过程通常依赖人工且耗时。受Graspit!启发——该工作利用仿真自动生成灵巧抓取姿态——我们提出Placeit!，一种基于进化计算的框架，用于生成刚性物体的有效放置位置。Placeit!具有高度灵活性，可支持从桌面放置到堆叠、插入等多种任务。实验表明，通过质量-多样性优化，Placeit!在所有场景下均显著优于当前最优方法，能生成多样化有效位姿。基于该框架构建的抓放流水线，在120次真实部署中达到90%成功率。本工作将Placeit!定位为开放环境抓放任务的强大工具，并可作为生成仿真基基础模型训练数据的关键引擎。
原文摘要 · Abstract (English)
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.
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