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2020 – today
- 2024
[c9]Chao Wang
, Stephan Hasler, Daniel Tanneberg, Felix Ocker, Frank Joublin, Antonello Ceravola, Joerg Deigmoeller, Michael Gienger:
LaMI: Large Language Models for Multi-Modal Human-Robot Interaction. CHI Extended Abstracts 2024: 218:1-218:10- [c8]Frank Joublin, Antonello Ceravola, Pavel Smirnov, Felix Ocker, Joerg Deigmoeller, Anna Belardinelli, Chao Wang, Stephan Hasler, Daniel Tanneberg, Michael Gienger:
CoPAL: Corrective Planning of Robot Actions with Large Language Models. ICRA 2024: 8664-8670 - [i13]Chao Wang, Stephan Hasler, Daniel Tanneberg, Felix Ocker, Frank Joublin, Antonello Ceravola, Joerg Deigmoeller, Michael Gienger:
Large Language Models for Multi-Modal Human-Robot Interaction. CoRR abs/2401.15174 (2024) - [i12]Daniel Tanneberg, Felix Ocker, Stephan Hasler, Joerg Deigmoeller, Anna Belardinelli, Chao Wang, Heiko Wersing, Bernhard Sendhoff, Michael Gienger:
To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions. CoRR abs/2403.12533 (2024) - [i11]Stephan Hasler, Daniel Tanneberg, Michael Gienger:
Efficient Symbolic Planning with Views. CoRR abs/2405.03307 (2024) - [i10]Felix Ocker, Daniel Tanneberg, Julian Eggert, Michael Gienger:
Tulip Agent - Enabling LLM-Based Agents to Solve Tasks Using Large Tool Libraries. CoRR abs/2407.21778 (2024) - 2023
- [c7]Chao Wang, Anna Belardinelli, Stephan Hasler, Theodoros Stouraitis, Daniel Tanneberg, Michael Gienger:
Explainable Human-Robot Training and Cooperation with Augmented Reality. CHI Extended Abstracts 2023: 449:1-449:5 - [c6]Daniel Tanneberg, Michael Gienger:
Learning Type-Generalized Actions for Symbolic Planning. IROS 2023: 2010-2017 - [i9]Chao Wang, Anna Belardinelli, Stephan Hasler, Theodoros Stouraitis, Daniel Tanneberg, Michael Gienger:
Explainable Human-Robot Training and Cooperation with Augmented Reality. CoRR abs/2302.01039 (2023) - [i8]Daniel Tanneberg, Michael Gienger:
Learning Type-Generalized Actions for Symbolic Planning. CoRR abs/2308.04867 (2023) - [i7]Frank Joublin, Antonello Ceravola, Pavel Smirnov, Felix Ocker, Joerg Deigmoeller, Anna Belardinelli, Chao Wang, Stephan Hasler, Daniel Tanneberg, Michael Gienger:
CoPAL: Corrective Planning of Robot Actions with Large Language Models. CoRR abs/2310.07263 (2023) - 2022
- [c5]Anna Belardinelli, Anirudh Reddy Kondapally, Dirk Ruiken, Daniel Tanneberg, Tomoki Watabe:
Intention estimation from gaze and motion features for human-robot shared-control object manipulation. IROS 2022: 9806-9813 - [i6]Anna Belardinelli, Anirudh Reddy Kondapally, Dirk Ruiken, Daniel Tanneberg, Tomoki Watabe:
Intention estimation from gaze and motion features for human-robot shared-control object manipulation. CoRR abs/2208.08688 (2022) - 2021
- [j4]Daniel Tanneberg, Kai Ploeger, Elmar Rueckert, Jan Peters:
SKID RAW: Skill Discovery From Raw Trajectories. IEEE Robotics Autom. Lett. 6(3): 4696-4703 (2021) - [i5]Daniel Tanneberg, Kai Ploeger, Elmar Rueckert, Jan Peters:
SKID RAW: Skill Discovery from Raw Trajectories. CoRR abs/2103.14610 (2021) - [i4]Daniel Tanneberg, Elmar Rueckert, Jan Peters:
Evolutionary Training and Abstraction Yields Algorithmic Generalization of Neural Computers. CoRR abs/2105.07957 (2021) - 2020
- [b1]Daniel Tanneberg:
Understand-Compute-Adapt: Neural Networks for Intelligent Agents. Technical University of Darmstadt, Germany, 2020 - [j3]Daniel Tanneberg, Elmar Rueckert, Jan Peters:
Evolutionary training and abstraction yields algorithmic generalization of neural computers. Nat. Mach. Intell. 2(12): 753-763 (2020) - [c4]Leon Keller, Daniel Tanneberg, Svenja Stark, Jan Peters:
Model-Based Quality-Diversity Search for Efficient Robot Learning. IROS 2020: 9675-9680 - [i3]Leon Keller, Daniel Tanneberg, Svenja Stark, Jan Peters:
Model-Based Quality-Diversity Search for Efficient Robot Learning. CoRR abs/2008.04589 (2020)
2010 – 2019
- 2019
- [j2]Daniel Tanneberg, Jan Peters, Elmar Rueckert:
Intrinsic motivation and mental replay enable efficient online adaptation in stochastic recurrent networks. Neural Networks 109: 67-80 (2019) - [i2]Daniel Tanneberg, Elmar Rueckert, Jan Peters:
Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer. CoRR abs/1911.00926 (2019) - 2018
- [i1]Daniel Tanneberg, Jan Peters, Elmar Rueckert:
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks. CoRR abs/1802.08013 (2018) - 2017
- [j1]Herke van Hoof, Daniel Tanneberg, Jan Peters:
Generalized exploration in policy search. Mach. Learn. 106(9-10): 1705-1724 (2017) - [c3]Daniel Tanneberg, Jan Peters, Elmar Rueckert:
Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals. CoRL 2017: 167-174 - [c2]Daniel Tanneberg, Jan Peters, Elmar Rueckert:
Efficient online adaptation with stochastic recurrent neural networks. Humanoids 2017: 198-204 - 2016
- [c1]Daniel Tanneberg, Alexandros Paraschos, Jan Peters, Elmar Rueckert:
Deep spiking networks for model-based planning in humanoids. Humanoids 2016: 656-661
Coauthor Index
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