5 papers
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,…
ReLI: A Language-Agnostic Approach to Human-Robot Interaction
Linus Nwankwo, Bjoern Ellensohn, Ozan Ãzdenizci +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, ensur…
Real-Time 3D Vision-Language Embedding Mapping
Christian Rauch, Björn Ellensohn, Linus Nwankwo +2
A metric-accurate semantic 3D representation is essential for many robotic tasks. This work proposes a simple, yet powerful, way to integrate the 2D embeddings of a Vision-Language…
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…