We tackle the challenge of robotic bin packing with irregular objects, such as groceries. Given the diverse physical attributes of these objects and the complex constraints governing their placement and manipulation, employing preprogrammed strategies becomes unfeasible. Our approach is to learn directly from expert demonstrations in order to extract implicit task knowledge and strategies to ensure safe object positioning, efficient use of space, and the generation of human-like behaviors that enhance human-robot trust. We rely on human demonstrations to learn a Markov chain for predicting the object packing sequence for a given set of items and then compare it with human performance. Our experimental results show that the model outperforms human performance by generating sequence predictions that humans classify as human-like more frequently than human-generated sequences. The human demonstrations were collected using our proposed VR platform, BoxED, which is a box packaging environment for simulating real-world objects and scenarios for fast and streamlined data collection with the purpose of teaching robots. We collected data from 43 participants packing a total of 263 boxes with supermarket-like objects, yielding 4644 object manipulations. Our VR platform can be easily adapted to new scenarios and objects, and is publicly available, alongside our dataset, at https://github.com/andrejfsantos4/BoxED.

翻译：我们针对不规则物体（如杂货）的机器人装箱挑战展开研究。考虑到这些物体多样的物理属性以及制约其放置与操作的复杂约束条件，采用预编程策略变得不可行。我们的方法是从专家示范中直接学习，以提取隐性任务知识与策略，确保物体定位安全、空间利用高效，并生成可增强人机信任的类人行为。我们依赖人类示范来学习一个用于预测给定物品装箱顺序的马尔可夫链，随后将其与人类表现进行比较。实验结果表明，该模型生成的序列预测被人类判定为类人行为的频率高于人类生成的序列，从而在性能上超越人类表现。人类示范数据通过我们提出的VR平台BoxED采集，该平台是一个用于模拟真实物体与场景的箱体包装环境，旨在实现快速高效的数据采集以训练机器人。我们从43名参与者处收集数据，共包装263个装载超市类物体的箱子，产生4644次物体操作。我们的VR平台可便捷适配新场景与新物体，且该平台与数据集已在https://github.com/andrejfsantos4/BoxED 公开提供。