12 citations · 15 across the 9 of their papers we have counts for
8 papers · 1 filter
SIL: Symbiotic Interactive Learning for Language-Conditioned Human-Agent Co-Adaptation
Linus Nwankwo, Bjoern Ellensohn, Christian Rauch +1
Today's autonomous agents, largely driven by foundation models (FMs), can understand natural language instructions and solve long-horizon tasks with human-like reasoning. However,…
VLEM: Real-Time 3D Vision-Language Embedding Mapping
Christian Rauch, Björn Ellensohn, Linus Nwankwo +2
Semantic scene understanding in robotics requires representations that are both metric-accurate and queryable via natural language in real-time. While recent Vision-Language Models…
ReLI: Cross-Lingual Language-to-Action Grounding for Human-Robot Interaction
Linus Nwankwo, Bjoern Ellensohn, Ozan Özdenizci +1
Adapting autonomous agents for real-world industrial, domestic, and other daily tasks is currently gaining momentum. However, in global or cross-lingual application contexts, the a…
EnvoDat: A Large-Scale Multisensory Dataset for Robotic Spatial Awareness and Semantic Reasoning in Heterogeneous Environments
Linus Nwankwo, Bjoern Ellensohn, Vedant Dave +5
To ensure the efficiency of robot autonomy under diverse real-world conditions, a high-quality heterogeneous dataset is essential to benchmark the operating algorithms' performance…
Multimodal Human-Autonomous Agents Interaction Using Pre-Trained Language and Visual Foundation Models
Linus Nwankwo, Elmar Rueckert
In this paper, we extended the method proposed in [21] to enable humans to interact naturally with autonomous agents through vocal and textual conversations. Our extended method ex…
The Conversation is the Command: Interacting with Real-World Autonomous Robot Through Natural Language
Linus Nwankwo, Elmar Rueckert
In recent years, autonomous agents have surged in real-world environments such as our homes, offices, and public spaces. However, natural human-robot interaction remains a key chal…