93 citations · 202 across the 37 of their papers we have counts for
6 papers · 1 filter
Learning to Autonomously Reach Objects with NICO and Grow-When-Required Networks
Nima Rahrakhshan, Matthias Kerzel, Philipp Allgeuer +2
The act of reaching for an object is a fundamental yet complex skill for a robotic agent, requiring a high degree of visuomotor control and coordination. In consideration of dynami…
Intelligent problem-solving as integrated hierarchical reinforcement learning
Manfred Eppe, Christian Gumbsch, Matthias Kerzel +3
According to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biological agents depends on hierarchical cognitive mechanisms. H…
Knowing Earlier what Right Means to You: A Comprehensive VQA Dataset for Grounding Relative Directions via Multi-Task Learning
Kyra Ahrens, Matthias Kerzel, Jae Hee Lee +2
Spatial reasoning poses a particular challenge for intelligent agents and is at the same time a prerequisite for their successful interaction and communication in the physical worl…
Learning Flexible Translation between Robot Actions and Language Descriptions
Ozan Özdemir, Matthias Kerzel, Cornelius Weber +2
Handling various robot action-language translation tasks flexibly is an essential requirement for natural interaction between a robot and a human. Previous approaches require chang…
What is Right for Me is Not Yet Right for You: A Dataset for Grounding Relative Directions via Multi-Task Learning
Jae Hee Lee, Matthias Kerzel, Kyra Ahrens +2
Understanding spatial relations is essential for intelligent agents to act and communicate in the physical world. Relative directions are spatial relations that describe the relati…
Language Model-Based Paired Variational Autoencoders for Robotic Language Learning
Ozan Özdemir, Matthias Kerzel, Cornelius Weber +2
Human infants learn language while interacting with their environment in which their caregivers may describe the objects and actions they perform. Similar to human infants, artific…