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Posts
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portfolio
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publications
Identifying Out-of-Distribution Samples in Real-Time for Safety-Critical 2D Object Detection with Margin Entropy Loss
Published in ECCV UNCV Workshop, 2022
Convolutional Neural Networks (CNNs) are nowadays often employed in vision-based perception stacks for safetycritical applications such as autonomous driving or Unmanned Aerial Vehicles (UAVs). Due to the safety requirements in those use cases, it is important to know the limitations of the CNN and, thus, to detect Out-of-Distribution (OOD) samples. In this work, we present an approach to enable OOD detection for 2D object detection by employing the margin entropy (ME) loss.
Recommended citation: Yannik Blei, Nicolas Jourdan, and Nils Gahlert. Identifying Out-of-Distribution Samples in Real-Time for Safety-Critical 2D Object Detection with Margin Entropy Loss. arXiv:2209.00364 [cs]. Sept. 2022 https://arxiv.org/pdf/2209.00364.pdf
LAN-grasp: Using Large Language Models for Semantic Object Grasping
Published in ISRR, 2023
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.
Recommended citation: Reihaneh Mirjalili, Michael Krawez, Simone Silenzi, Yannik Blei, and Wolfram Burgard. Lan-grasp: Using large language models for semantic object grasping. arXiv:2310.05239, Oct 2023 https://arxiv.org/pdf/2311.17776.pdf
CloudTrack: Scalable UAV Tracking with Cloud Semantics
Published in ICRA, 2024
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. We publish the methods source code at https://github.com/yblei/CloudTrack.
Recommended citation: Yannik Blei, Michael Krawez, Nisarga Nilavadi, Tanja Katharina Kaiser and Wolfram Burgard. CloudTrack: Scalable UAV Tracking with Cloud Semantics. arXiv:2409.16111, Oct 2024 https://arxiv.org/pdf/2409.16111
CloudTrack: Scalable UAV Tracking with Cloud Semantics
Published in ICRA, 2025
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. We publish the methods source code at https://github.com/yblei/CloudTrack.
Recommended citation: Yannik Blei, Michael Krawez, Nisarga Nilavadi, Tanja Katharina Kaiser and Wolfram Burgard. CloudTrack: Scalable UAV Tracking with Cloud Semantics. Accepted to ICRA, Mai 2025 https://arxiv.org/pdf/2409.16111
LLM-Pack: Intuitive Grocery Handling for Logistics Applications
Published in arxiv, 2025
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.
Recommended citation: Yannik Blei, Michael Krawez, Tobias Jülg, Pierre Krack, Florian Walter and Wolfram Burgard. LLM-Pack: Intuitive Grocery Handling for Logistics Applications. arXiv:2503.08445, Mar 2025 https://arxiv.org/pdf/2503.08445
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Artificial Intelligence Basic Module
Graduate course (TA), Responsible Professor: Wolfram Burgard, Technische Universität Nürnberg (UTN), 2023
This course introduced basic concepts and techniques used within the field of Artificial Intelligence. The course focused on fundamental methods like search algorithms, propositional and predicate logic, and probabilistic approaches. At the end of the course, also machine learning strategies were covered. Among other topics, we discussed:
- Problem formalization
- Finding solutions by searching
- Board games
- Logic
- Decision-making and acting under uncertainty
- Machine learning
- Ethics in Artificial Intelligence
Introduction to Mobile Robotics
Graduate course (TA), Responsible Professor: Wolfram Burgard, Technische Universität Nürnberg (UTN), 2023
This course introduced basic concepts and techniques used within the field of mobile robotics. The course focused on the fundamental challenges for autonomous intelligent systems and presented the state-of-the-art solutions. The course focus lied on probabilistic approaches to robot state estimation. Among other topics, we discussed:
- Kinematics
- Sensors
- Probabilistic modelling
- Robot localization
- Mapping
- SLAM
- Path Planning
- Ethics in Robotics