62 citations · 113 across the 8 of their papers we have counts for
8 papers
Language-Grounded Dynamic Scene Graphs for Interactive Object Search with Mobile Manipulation
Daniel Honerkamp, Martin Büchner, Fabien Despinoy +2
To fully leverage the capabilities of mobile manipulation robots, it is imperative that they are able to autonomously execute long-horizon tasks in large unexplored environments. W…
Learning Hierarchical Interactive Multi-Object Search for Mobile Manipulation
Fabian Schmalstieg, Daniel Honerkamp, Tim Welschehold +1
Existing object-search approaches enable robots to search through free pathways, however, robots operating in unstructured human-centered environments frequently also have to manip…
Active Particle Filter Networks: Efficient Active Localization in Continuous Action Spaces and Large Maps
Daniel Honerkamp, Suresh Guttikonda, Abhinav Valada
Accurate localization is a critical requirement for most robotic tasks. The main body of existing work is focused on passive localization in which the motions of the robot are assu…
NM: Learning Navigation for Arbitrary Mobile Manipulation Motions in Unseen and Dynamic Environments
Daniel Honerkamp, Tim Welschehold, Abhinav Valada
Despite its importance in both industrial and service robotics, mobile manipulation remains a significant challenge as it requires a seamless integration of end-effector trajectory…
Learning Long-Horizon Robot Exploration Strategies for Multi-Object Search in Continuous Action Spaces
Fabian Schmalstieg, Daniel Honerkamp, Tim Welschehold +1
Recent advances in vision-based navigation and exploration have shown impressive capabilities in photorealistic indoor environments. However, these methods still struggle with long…
Catch Me If You Hear Me: Audio-Visual Navigation in Complex Unmapped Environments with Moving Sounds
Abdelrahman Younes, Daniel Honerkamp, Tim Welschehold +1
Audio-visual navigation combines sight and hearing to navigate to a sound-emitting source in an unmapped environment. While recent approaches have demonstrated the benefits of audi…
Learning Kinematic Feasibility for Mobile Manipulation through Deep Reinforcement Learning
Daniel Honerkamp, Tim Welschehold, Abhinav Valada
Mobile manipulation tasks remain one of the critical challenges for the widespread adoption of autonomous robots in both service and industrial scenarios. While planning approaches…
Democratising blockchain: A minimal agency consensus model
Marcin Abram, David Galindo, Daniel Honerkamp +2
We propose a novel consensus protocol based on a hybrid approach, that combines a directed acyclic graph (DAG) and a classical chain of blocks. This architecture allows us to enfor…