论文标题
您需要的所有包装：学习一种可推广的袋装策略，以实现异质物体
Bag All You Need: Learning a Generalizable Bagging Strategy for Heterogeneous Objects
论文作者
论文摘要
我们引入了一种实用的机器人解决方案，以实现异质行李的任务，需要将多个刚性和可变形的物体放置在一个可变形的袋中。这是一项艰巨的任务，因为它在有限的可观察性下具有多个高度可变形的对象之间的复杂交互。为了应对这些挑战，我们提出了一个由两种学识渊博的政策组成的机器人系统：一个重新安排政策，该政策学会了放置多个刚性对象和折叠可变形的物体，以达到理想的预击前效果条件，以及提升政策，以推断出适当的抓地点，以供双人袋提起。我们在现实世界中的三臂机器人平台上评估了这些学习的政策，该平台可通过新物体获得70％的异质行李成功率。为了促进未来的研究和比较，我们还开发了一种新型的异质行李模拟基准，该基准将公开可用。
We introduce a practical robotics solution for the task of heterogeneous bagging, requiring the placement of multiple rigid and deformable objects into a deformable bag. This is a difficult task as it features complex interactions between multiple highly deformable objects under limited observability. To tackle these challenges, we propose a robotic system consisting of two learned policies: a rearrangement policy that learns to place multiple rigid objects and fold deformable objects in order to achieve desirable pre-bagging conditions, and a lifting policy to infer suitable grasp points for bi-manual bag lifting. We evaluate these learned policies on a real-world three-arm robot platform that achieves a 70% heterogeneous bagging success rate with novel objects. To facilitate future research and comparison, we also develop a novel heterogeneous bagging simulation benchmark that will be made publicly available.
