6 citations · 8 across the 10 of their papers we have counts for
10 papers
SIR: Structured Image Representations for Explainable Robot Learning
Paul Mattes, Jan Schwab, Jens Bosch +5
Existing robot policies based on learned visual embeddings lack explicit structure and are sensitive to visual distractions. Thus, the representations that drive their behaviour ar…
SPARC: Reliable Spatial Annotations from Robot Demonstrations at Scale
Nils Blank, Paul Mattes, Maximilian Xiling Li +5
This work introduces Spatial Annotations from Robot Demonstrations with Reliability Calibration (SPARC), a risk-aware framework that automatically labels robot demonstrations with…
DuoBench: A Reproducible Benchmark for Bimanual Manipulation in Simulation and the Real World
Tobias Jülg, Seongjin Bien, Simon Hilber +7
Bimanual robot systems substantially expand manipulation capabilities, but coordinating two arms introduces additional control complexity and failure modes that are not well captur…
Interpretable Affordance Detection on 3D Point Clouds with Probabilistic Prototypes
Maximilian Xiling Li, Korbinian Rudolf, Nils Blank +1
Robotic agents need to understand how to interact with objects in their environment, both autonomously and during human-robot interactions. Affordance detection on 3D point clouds,…
An Overview of Prototype Formulations for Interpretable Deep Learning
Maximilian Xiling Li, Korbinian Franz Rudolf, Paul Mattes +2
Prototypical part networks offer interpretable alternatives to black-box deep learning models by learning visual prototypes for classification. This work provides a comprehensive a…
Endless Jailbreaks with Bijection Learning
Brian R. Y. Huang, Maximilian Li, Leonard Tang
Despite extensive safety measures, LLMs are vulnerable to adversarial inputs, or jailbreaks, which can elicit unsafe behaviors. In this work, we introduce bijection learning, a pow…
Use the Force, Bot! -- Force-Aware ProDMP with Event-Based Replanning
Paul Werner Lödige, Maximilian Xiling Li, Rudolf Lioutikov
Movement Primitives (MPs) are a well-established method for representing and generating modular robot trajectories. This work presents FA-ProDMP, a new approach which introduces fo…
Optimal ablation for interpretability
Maximilian Li, Lucas Janson
Interpretability studies often involve tracing the flow of information through machine learning models to identify specific model components that perform relevant computations for…
Planning in a recurrent neural network that plays Sokoban
Mohammad Taufeeque, Philip Quirke, Maximilian Li +4
Planning is essential for solving complex tasks, yet the internal mechanisms underlying planning in neural networks remain poorly understood. Building on prior work, we analyze a r…
Circuit Breaking: Removing Model Behaviors with Targeted Ablation
Maximilian Li, Xander Davies, Max Nadeau
Language models often exhibit behaviors that improve performance on a pre-training objective but harm performance on downstream tasks. We propose a novel approach to removing undes…