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.

翻译：我们提出了一种面向异构装袋任务的实用机器人解决方案，该任务要求将多个刚性和可变形物体放入可变形袋子中。这是一项艰巨的任务，因为在有限可观测性条件下，多个高度可变形物体之间存在复杂的交互作用。为应对这些挑战，我们提出了一套由两个学习型策略组成的机器人系统：一个是重排策略，通过学习放置多个刚性物体并折叠可变形物体以实现理想的装袋前状态；另一个是提拉策略，用于推断双臂提袋的合适抓取点。我们在一套真实世界三臂机器人平台上评估了这些学习型策略，对未知物体实现了70%的异构装袋成功率。为促进未来研究与比较，我们还开发了一个新颖的异构装袋仿真基准测试平台，该平台将公开发布。