27 citations · 44 across the 22 of their papers we have counts for
9 papers · 1 filter
Concept-Based Explanations in Computer Vision: Where Are We and Where Could We Go?
Jae Hee Lee, Georgii Mikriukov, Gesina Schwalbe +2
Concept-based XAI (C-XAI) approaches to explaining neural vision models are a promising field of research, since explanations that refer to concepts (i.e., semantically meaningful…
Robots Can Multitask Too: Integrating a Memory Architecture and LLMs for Enhanced Cross-Task Robot Action Generation
Hassan Ali, Philipp Allgeuer, Carlo Mazzola +4
Large Language Models (LLMs) have been recently used in robot applications for grounding LLM common-sense reasoning with the robot's perception and physical abilities. In humanoid…
Unconstrained Open Vocabulary Image Classification: Zero-Shot Transfer from Text to Image via CLIP Inversion
Philipp Allgeuer, Kyra Ahrens, Stefan Wermter
We introduce NOVIC, an innovative real-time uNconstrained Open Vocabulary Image Classifier that uses an autoregressive transformer to generatively output classification labels as l…
When Robots Get Chatty: Grounding Multimodal Human-Robot Conversation and Collaboration
Philipp Allgeuer, Hassan Ali, Stefan Wermter
We investigate the use of Large Language Models (LLMs) to equip neural robotic agents with human-like social and cognitive competencies, for the purpose of open-ended human-robot c…
Unified Dynamic Scanpath Predictors Outperform Individually Trained Neural Models
Fares Abawi, Di Fu, Stefan Wermter
Previous research on scanpath prediction has mainly focused on group models, disregarding the fact that the scanpaths and attentional behaviors of individuals are diverse. The disr…
Inverse Kinematics for Neuro-Robotic Grasping with Humanoid Embodied Agents
Jan-Gerrit Habekost, Connor Gäde, Philipp Allgeuer +1
This paper introduces a novel zero-shot motion planning method that allows users to quickly design smooth robot motions in Cartesian space. A Bézier curve-based Cartesian plan is t…
Comparing Apples to Oranges: LLM-powered Multimodal Intention Prediction in an Object Categorization Task
Hassan Ali, Philipp Allgeuer, Stefan Wermter
Human intention-based systems enable robots to perceive and interpret user actions to interact with humans and adapt to their behavior proactively. Therefore, intention prediction…
Human Impression of Humanoid Robots Mirroring Social Cues
Di Fu, Fares Abawi, Philipp Allgeuer +1
Mirroring non-verbal social cues such as affect or movement can enhance human-human and human-robot interactions in the real world. The robotic platforms and control methods also i…
Causal State Distillation for Explainable Reinforcement Learning
Wenhao Lu, Xufeng Zhao, Thilo Fryen +4
Reinforcement learning (RL) is a powerful technique for training intelligent agents, but understanding why these agents make specific decisions can be quite challenging. This lack…