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This paper introduces a contact-aware bin-packing approach for partially filled containers, addressing a limitation of prior work focused on empty containers and collision-free strategies. They use a contact-based multi-object trajectory optimizer within a model predictive controller to purposefully interact with existing objects to create space for new items. The system integrates a physics-aware perception system for pose estimation during occlusions and a method for suggesting physically-feasible placement locations.
Robots can now Tetris in the real world, intelligently nudging existing objects to create space for new ones in partially filled containers.
The automation of warehouse operations is crucial for improving productivity and reducing human exposure to hazardous environments. One operation frequently performed in warehouses is bin-packing where items need to be placed into containers, either for delivery to a customer, or for temporary storage in the warehouse. Whilst prior bin-packing works have largely been focused on packing items into empty containers and have adopted collision-free strategies, it is often the case that containers will already be partially filled with items, often in suboptimal arrangements due to transportation about a warehouse. This paper presents a contact-aware packing approach that exploits purposeful interactions with previously placed objects to create free space and enable successful placement of new items. This is achieved by using a contact-based multi-object trajectory optimizer within a model predictive controller, integrated with a physics-aware perception system that estimates object poses even during inevitable occlusions, and a method that suggests physically-feasible locations to place the object inside the container.