1 citations · 1 across the 1 of their papers we have counts for
9 papers
Embodied Neurocomputation: A Framework for Interfacing Biological Neural Cultures with Scaled Task-Driven Validation
Johnson Zhou, Daniel Tanneberg, Forough Habibollahi +12
Biological neural networks (BNNs) have been established as a powerful and adaptive substrate that offer the potential for incredibly energy and data efficient information processin…
MERGE: Guided Vision-Language Models for Multi-Actor Event Reasoning and Grounding in Human-Robot Interaction
Joerg Deigmoeller, Nakul Agarwal, Stephan Hasler +8
We introduce MERGE, a system for situational grounding of actors, objects, and events in dynamic human-robot group interactions. Effective collaboration in such settings requires c…
Neuro-Symbolic Imitation Learning: Discovering Symbolic Abstractions for Skill Learning
Leon Keller, Daniel Tanneberg, Jan Peters
Imitation learning is a popular method for teaching robots new behaviors. However, most existing methods focus on teaching short, isolated skills rather than long, multi-step tasks…
Local Pairwise Distance Matching for Backpropagation-Free Reinforcement Learning
Daniel Tanneberg
Training neural networks with reinforcement learning (RL) typically relies on backpropagation (BP), necessitating storage of activations from the forward pass for subsequent backwa…
CARMA: Context-Aware Situational Grounding of Human-Robot Group Interactions by Combining Vision-Language Models with Object and Action Recognition
Joerg Deigmoeller, Stephan Hasler, Nakul Agarwal +8
We introduce CARMA, a system for situational grounding in human-robot group interactions. Effective collaboration in such group settings requires situational awareness based on a c…
Mirror Eyes: Explainable Human-Robot Interaction at a Glance
Matti Krüger, Daniel Tanneberg, Chao Wang +2
The gaze of a person tends to reflect their interest. This work explores what happens when this statement is taken literally and applied to robots. Here we present a robot system t…