论文标题
机器人学习人类示范中包装不规则对象的顺序
Robotic Learning the Sequence of Packing Irregular Objects from Human Demonstrations
论文作者
论文摘要
我们应对杂货等不规则物体的机器人垃圾箱包装的挑战。考虑到这些对象的各种物理属性以及采用预编程策略的复杂限制是不可行的。我们的方法是直接从专家演示中学习，以提取隐性任务知识和策略，以确保对象定位，有效地利用空间以及增强人类机器人信任的类似人类行为。 我们依靠人类的示范来学习马尔可夫链来预测给定项目的对象填料序列，然后将其与人类的绩效进行比较。我们的实验结果表明，该模型通过产生人类比人类生成的序列更频繁地将人类归类为人类样的序列预测来优于人类表现。 使用我们提出的VR平台Boxed收集了人类示范，该平台是一个盒装包装环境，用于模拟现实世界中的对象和场景，以进行快速和简化的数据收集，目的是教授机器人。我们从43位参与者中收集了数据，这些数据总共包装了263个盒子，这些盒子带有超市样品，产生了4644件对象操作。我们的VR平台可以很容易地适应新的方案和对象，并在https://github.com/andrejfsantos4/boxed上公开可用。
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.
