Daniel Tanneberg — accepted AI-conference papers

2026

MERGE: Guided Vision-Language Models for Multi-Actor Event Reasoning and Grounding in Human–Robot Interaction

Joerg Deigmoeller, Nakul Agarwal, Stephan Hasler, Daniel Tanneberg, Anna Belardinelli, Reza Ghoddoosian +5

ICRA 2026poster

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 consistent situational awareness, built on persistent representations of people and objects and an episodic abstraction o…

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2025

Neuro-Symbolic Imitation Learning: Discovering Symbolic Abstractions for Skill Learning

Leon Keller, Daniel Tanneberg, Jan Peters

ICRA 2025

Imitation learning is a popular method for teaching robots new behaviors. However, most existing methods focus on teaching short, isolated skills rather than long, multistep tasks. To bridge this gap, imitation learning algorithms must not only learn individual skills but also an abstract understand

Cited by 7 Source ↗Scholar ↗

2024

CoPAL: Corrective Planning of Robot Actions with Large Language Models

Frank Joublin, Antonello Ceravola, Pavel Smirnov, Felix Ocker, Joerg Deigmoeller, Anna Belardinelli +4

ICRA 2024poster

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 challenge. Addressing this imperative, this study contributes to the field of Large Language Models (LLMs) applied to task…

Cited by 51 Source ↗Scholar ↗

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 +3

IROS 2024poster

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 combines scene perception, dialogue acquisition, situation understanding, and behavior generation with the common-sense…

Cited by 16 Source ↗Scholar ↗

2023

Learning Type-Generalized Actions for Symbolic Planning

Daniel Tanneberg, Michael Gienger

IROS 2023poster

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 this approach is the necessity for suitable symbolic representations describing the state of the environment as well as th…

Cited by 7 Source ↗Scholar ↗

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

IROS 2022poster

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, which relies on behavioral observations. Here, an intention estimation framework is presented, which uses natural gaze a…

Cited by 19 Source ↗Scholar ↗

2021

SKID RAW: Skill Discovery From Raw Trajectories

Daniel Tanneberg, Kai Ploeger, Elmar Rueckert, Jan Peters

RA-L 2021

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 them a new task in an easy and natural way. When teaching tasks that involve sequences of different skills, with varying

Cited by 35 Source ↗Scholar ↗

2020

Model-Based Quality-Diversity Search for Efficient Robot Learning

Leon Keller, Daniel Tanneberg, Svenja Stark, Jan Peters

IROS 2020poster

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 autonomously generate a repertoire of diverse skills is a novelty based Quality-Diversity (QD) algorithm. However, as m…

Cited by 26 Source ↗Scholar ↗

2017

Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals

Daniel Tanneberg, Jan Peters, Elmar Rueckert

CoRL 2017

Continuous online adaptation is an essential ability for the vision of fully autonomous and lifelong-learning robots. Robots need to be able to adapt to changing environments and constraints while this adaption should be performed without interrupting the robot’s motion. In this paper, we introduce

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