LLM-Pack: Intuitive Grocery Handling for Logistics Applications

Robotics and automation are increasingly influential in logistics but remain largely confined to traditional warehouses. In grocery retail, advancements such as cashier-less supermarkets exist, yet customers still manually pick and pack groceries. While there has been a substantial focus in robotics on the bin picking problem, the task of packing objects and groceries has remained largely untouched. However, packing grocery items in the right order is crucial for preventing product damage, e.g., heavy objects should not be placed on top of fragile ones. However, the exact criteria for the right packing order are hard to define, in particular given the huge variety of objects typically found in stores. In this paper, we introduce LLM-Pack, a novel approach for grocery packing. LLM-Pack leverages language and vision foundation models for identifying groceries and generating a packing sequence that mimics human packing strategy. LLM-Pack does not require dedicated training to handle new grocery items and its modularity allows easy upgrades of the underlying foundation models. We extensively evaluate our approach to demonstrate its performance. We will make the source code of LLMPack publicly available upon the publication of this manuscript.

CloudTrack: Scalable UAV Tracking with Cloud Semantics

Nowadays, unmanned aerial vehicles (UAVs) are commonly used in search and rescue scenarios to gather information in the search area. The automatic identification of the person searched for in aerial footage could increase the autonomy of such systems, reduce the search time, and thus increase the missed person's chances of survival. In this paper, we present a novel approach to perform semantically conditioned open vocabulary object tracking that is specifically designed to cope with the limitations of UAV hardware. Our approach has several advantages. It can run with verbal descriptions of the missing person, e.g., the color of the shirt, it does not require dedicated training to execute the mission and can efficiently track a potentially moving person. Our experimental results demonstrate the versatility and efficacy of our approach.

LAN-grasp: Using Large Language Models for Semantic Object Grasping

In this paper, we propose LAN-grasp, a novel approach towards more appropriate semantic grasping. We use foundation models to provide the robot with a deeper understanding of the objects, the right place to grasp an object, or even the parts to avoid. This allows our robot to grasp and utilize objects in a more meaningful and safe manner. We leverage the combination of a Large Language Model, a Vision Language Model, and a traditional grasp planner to generate grasps demonstrating a deeper semantic understanding of the objects. We first prompt the Large Language Model about which object part is appropriate for grasping. Next, the Vision Language Model identifies the corresponding part in the object image. Finally, we generate grasp proposals in the region proposed by the Vision Language Model. Building on foundation models provides us with a zero-shot grasp method that can handle a wide range of objects without the need for further training or fine-tuning. We evaluated our method in real-world experiments on a custom object data set. We present the results of a survey that asks the participants to choose an object part appropriate for grasping. The results show that the grasps generated by our method are consistently ranked higher by the participants than those generated by a conventional grasping planner and a recent semantic grasping approach.