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We introduce a practical robotics solution for the task of heterogeneousbagging, requiring the placement of multiple rigid and deformable objects intoa deformable bag. This is a difficult task as it features complex interactionsbetween multiple highly deformable objects under limited observability. Totackle these challenges, we propose a robotic system consisting of two learnedpolicies: a rearrangement policy that learns to place multiple rigid objectsand fold deformable objects in order to achieve desirable pre-baggingconditions, and a lifting policy to infer suitable grasp points for bi-manualbag lifting. We evaluate these learned policies on a real-world three-arm robotplatform that achieves a 70% heterogeneous bagging success rate with novelobjects. To facilitate future research and comparison, we also develop a novelheterogeneous bagging simulation benchmark that will be made publiclyavailable.

Bag All You Need: Learning a Generalizable Bagging Strategy for Heterogeneous Objects