Daniel Honerkamp
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View article: MORE: Mobile Manipulation Rearrangement Through Grounded Language Reasoning
MORE: Mobile Manipulation Rearrangement Through Grounded Language Reasoning Open
Autonomous long-horizon mobile manipulation encompasses a multitude of challenges, including scene dynamics, unexplored areas, and error recovery. Recent works have leveraged foundation models for scene-level robotic reasoning and planning…
View article: Task-Driven Co-Design of Mobile Manipulators
Task-Driven Co-Design of Mobile Manipulators Open
Recent interest in mobile manipulation has resulted in a wide range of new robot designs. A large family of these designs focuses on modular platforms that combine existing mobile bases with static manipulator arms. They combine these modu…
View article: Whole-Body Teleoperation for Mobile Manipulation at Zero Added Cost
Whole-Body Teleoperation for Mobile Manipulation at Zero Added Cost Open
Demonstration data plays a key role in learning complex behaviors and training robotic foundation models. While effective control interfaces exist for static manipulators, data collection remains cumbersome and time intensive for mobile ma…
View article: Reinforcement learning as a robotics-inspired framework for insect navigation: from spatial representations to neural implementation
Reinforcement learning as a robotics-inspired framework for insect navigation: from spatial representations to neural implementation Open
Bees are among the master navigators of the insect world. Despite impressive advances in robot navigation research, the performance of these insects is still unrivaled by any artificial system in terms of training efficiency and generaliza…
View article: Language-Grounded Dynamic Scene Graphs for Interactive Object Search With Mobile Manipulation
Language-Grounded Dynamic Scene Graphs for Interactive Object Search With Mobile Manipulation Open
To fully leverage the capabilities of mobile manipulation robots, it is\nimperative that they are able to autonomously execute long-horizon tasks in\nlarge unexplored environments. While large language models (LLMs) have shown\nemergent re…
View article: Perception Matters: Enhancing Embodied AI with Uncertainty-Aware Semantic Segmentation
Perception Matters: Enhancing Embodied AI with Uncertainty-Aware Semantic Segmentation Open
Embodied AI has made significant progress acting in unexplored environments. However, tasks such as object search have largely focused on efficient policy learning. In this work, we identify several gaps in current search methods: They lar…
View article: Reinforcement Learning as a Robotics-Inspired Framework for Insect Navigation: From Spatial Representations to Neural Implementation
Reinforcement Learning as a Robotics-Inspired Framework for Insect Navigation: From Spatial Representations to Neural Implementation Open
Bees are among the master navigators of the insect world. Despite impressive advances in robot navigation research, the performance of these insects is still unrivaled by any artificial system in terms of training efficiency and generaliza…
View article: Learning Hierarchical Interactive Multi-Object Search for Mobile Manipulation
Learning Hierarchical Interactive Multi-Object Search for Mobile Manipulation Open
Existing object-search approaches enable robots to search through free pathways, however, robots operating in unstructured human-centered environments frequently also have to manipulate the environment to their needs. In this work, we intr…
View article: Active Particle Filter Networks: Efficient Active Localization in Continuous Action Spaces and Large Maps
Active Particle Filter Networks: Efficient Active Localization in Continuous Action Spaces and Large Maps Open
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 assumed given, abstracting from their influence on sampling inf…
View article: N$^2$M$^2$: Learning Navigation for Arbitrary Mobile Manipulation Motions in Unseen and Dynamic Environments
N$^2$M$^2$: Learning Navigation for Arbitrary Mobile Manipulation Motions in Unseen and Dynamic Environments Open
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 generation with navigation skills as well as reasoning ove…
View article: Learning Long-Horizon Robot Exploration Strategies for Multi-Object Search in Continuous Action Spaces
Learning Long-Horizon Robot Exploration Strategies for Multi-Object Search in Continuous Action Spaces Open
Recent advances in vision-based navigation and exploration have shown impressive capabilities in photorealistic indoor environments. However, these methods still struggle with long-horizon tasks and require large amounts of data to general…
View article: Catch Me If You Hear Me: Audio-Visual Navigation in Complex Unmapped Environments with Moving Sounds
Catch Me If You Hear Me: Audio-Visual Navigation in Complex Unmapped Environments with Moving Sounds Open
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 audio input to detect and find the goal, they focus on clean an…
View article: Learning Kinematic Feasibility for Mobile Manipulation Through Deep Reinforcement Learning
Learning Kinematic Feasibility for Mobile Manipulation Through Deep Reinforcement Learning Open
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 are good at generating feasible whole-body robot trajector…
View article: Learning Kinematic Feasibility for Mobile Manipulation through Deep\n Reinforcement Learning
Learning Kinematic Feasibility for Mobile Manipulation through Deep\n Reinforcement Learning Open
Mobile manipulation tasks remain one of the critical challenges for the\nwidespread adoption of autonomous robots in both service and industrial\nscenarios. While planning approaches are good at generating feasible whole-body\nrobot trajec…
View article: Democratising blockchain: A minimal agency consensus model
Democratising blockchain: A minimal agency consensus model Open
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 enforce collective block construction, minimising the monopolist…