72 citations · 201 across the 18 of their papers we have counts for
19 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…
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…
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…
Tulip Agent -- Enabling LLM-Based Agents to Solve Tasks Using Large Tool Libraries
Felix Ocker, Daniel Tanneberg, Julian Eggert +1
We introduce tulip agent, an architecture for autonomous LLM-based agents with Create, Read, Update, and Delete access to a tool library containing a potentially large number of to…
Efficient Symbolic Planning with Views
Stephan Hasler, Daniel Tanneberg, Michael Gienger
Robotic planning systems model spatial relations in detail as these are needed for manipulation tasks. In contrast to this, other physical attributes of objects and the effect of d…
To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions
Daniel Tanneberg, Felix Ocker, Stephan Hasler +6
How can a robot provide unobtrusive physical support within a group of humans? We present Attentive Support, a novel interaction concept for robots to support a group of humans. It…
LaMI: Large Language Models for Multi-Modal Human-Robot Interaction
Chao Wang, Stephan Hasler, Daniel Tanneberg +5
This paper presents an innovative large language model (LLM)-based robotic system for enhancing multi-modal human-robot interaction (HRI). Traditional HRI systems relied on complex…
CoPAL: Corrective Planning of Robot Actions with Large Language Models
Frank Joublin, Antonello Ceravola, Pavel Smirnov +7
In the pursuit of fully autonomous robotic systems capable of taking over tasks traditionally performed by humans, the complexity of open-world environments poses a considerable ch…
Learning Type-Generalized Actions for Symbolic Planning
Daniel Tanneberg, Michael Gienger
Symbolic planning is a powerful technique to solve complex tasks that require long sequences of actions and can equip an intelligent agent with complex behavior. The downside of th…
Explainable Human-Robot Training and Cooperation with Augmented Reality
Chao Wang, Anna Belardinelli, Stephan Hasler +3
The current spread of social and assistive robotics applications is increasingly highlighting the need for robots that can be easily taught and interacted with, even by users with…
Intention estimation from gaze and motion features for human-robot shared-control object manipulation
Anna Belardinelli, Anirudh Reddy Kondapally, Dirk Ruiken +2
Shared control can help in teleoperated object manipulation by assisting with the execution of the user's intention. To this end, robust and prompt intention estimation is needed,…
Evolutionary Training and Abstraction Yields Algorithmic Generalization of Neural Computers
Daniel Tanneberg, Elmar Rueckert, Jan Peters
A key feature of intelligent behaviour is the ability to learn abstract strategies that scale and transfer to unfamiliar problems. An abstract strategy solves every sample from a p…
SKID RAW: Skill Discovery from Raw Trajectories
Daniel Tanneberg, Kai Ploeger, Elmar Rueckert +1
Integrating robots in complex everyday environments requires a multitude of problems to be solved. One crucial feature among those is to equip robots with a mechanism for teaching…
Model-Based Quality-Diversity Search for Efficient Robot Learning
Leon Keller, Daniel Tanneberg, Svenja Stark +1
Despite recent progress in robot learning, it still remains a challenge to program a robot to deal with open-ended object manipulation tasks. One approach that was recently used to…
Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer Architecture
Daniel Tanneberg, Elmar Rueckert, Jan Peters
A key feature of intelligent behavior is the ability to learn abstract strategies that transfer to unfamiliar problems. Therefore, we present a novel architecture, based on memory-…
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks
Daniel Tanneberg, Jan Peters, Elmar Rueckert
Autonomous robots need to interact with unknown, unstructured and changing environments, constantly facing novel challenges. Therefore, continuous online adaptation for lifelong-le…