Daniel Tanneberg

According to our database, Daniel Tanneberg authored at least 16 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions.

Daniel Tanneberg, Felix Ocker, Stephan Hasler, Joerg Deigmoeller, Anna Belardinelli, Chao Wang, Heiko Wersing, Bernhard Sendhoff, Michael Gienger

CoRR, 2024

Large Language Models for Multi-Modal Human-Robot Interaction.

Chao Wang, Stephan Hasler, Daniel Tanneberg, Felix Ocker, Frank Joublin, Antonello Ceravola, Joerg Deigmoeller, Michael Gienger

CoRR, 2024

2023

CoPAL: Corrective Planning of Robot Actions with Large Language Models.

Frank Joublin, Antonello Ceravola, Pavel Smirnov, Felix Ocker, Joerg Deigmoeller, Anna Belardinelli, Chao Wang, Stephan Hasler, Daniel Tanneberg, Michael Gienger

CoRR, 2023

Learning Type-Generalized Actions for Symbolic Planning.

Daniel Tanneberg, Michael Gienger

IROS, 2023

Explainable Human-Robot Training and Cooperation with Augmented Reality.

Chao Wang, Anna Belardinelli, Stephan Hasler, Theodoros Stouraitis, Daniel Tanneberg, Michael Gienger

Proceedings of the Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, 2023

2022

Intention estimation from gaze and motion features for human-robot shared-control object manipulation.

Anna Belardinelli, Anirudh Reddy Kondapally, Dirk Ruiken, Daniel Tanneberg, Tomoki Watabe

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

2021

SKID RAW: Skill Discovery From Raw Trajectories.

Daniel Tanneberg, Kai Ploeger, Elmar Rueckert, Jan Peters

IEEE Robotics Autom. Lett., 2021

2020

Understand-Compute-Adapt: Neural Networks for Intelligent Agents.

Daniel Tanneberg

PhD thesis, 2020

Evolutionary training and abstraction yields algorithmic generalization of neural computers.

Daniel Tanneberg, Elmar Rueckert, Jan Peters

Nat. Mach. Intell., 2020

Model-Based Quality-Diversity Search for Efficient Robot Learning.

Leon Keller, Daniel Tanneberg, Svenja Stark, Jan Peters

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

2019

Intrinsic motivation and mental replay enable efficient online adaptation in stochastic recurrent networks.

Daniel Tanneberg, Jan Peters, Elmar Rueckert

Neural Networks, 2019

Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer.

Daniel Tanneberg, Elmar Rueckert, Jan Peters

CoRR, 2019

2017

Generalized exploration in policy search.

Herke van Hoof, Daniel Tanneberg, Jan Peters

Mach. Learn., 2017

Efficient online adaptation with stochastic recurrent neural networks.

Daniel Tanneberg, Jan Peters, Elmar Rueckert

Proceedings of the 17th IEEE-RAS International Conference on Humanoid Robotics, 2017

Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals.

Daniel Tanneberg, Jan Peters, Elmar Rueckert

Proceedings of the 1st Annual Conference on Robot Learning, CoRL 2017, Mountain View, 2017

2016

Deep spiking networks for model-based planning in humanoids.

Daniel Tanneberg, Alexandros Paraschos, Jan Peters, Elmar Rueckert

Proceedings of the 16th IEEE-RAS International Conference on Humanoid Robots, 2016