17 citations · 20 across the 8 of their papers we have counts for
10 papers
DreamFormer: Dream Imitation with a Transformer World Model for Language-Conditioned Robotic Manipulation
Mostafa Kotb, Cornelius Weber, Muhammad Burhan Hafez +1
We introduce DreamFormer, a model-based agent that acquires language-conditioned, multi-task skills by imitating expert demonstrations within the latent imagination of a learned wo…
MorphIK: Morphology-Conditioned Neural Inverse Kinematics for Unknown Robots
Lennart Clasmeier, Jan Gerrit Habekost, Cornelius Weber +1
Neural models can learn to generate various solutions to the inverse kinematics problem from data, but are usually limited to a single robot. We present MorphIK, a flow-matching mo…
Theory of Mind for Explainable Human-Robot Interaction
Marie S. Bauer, Julia Gachot, Matthias Kerzel +2
Within the context of human-robot interaction (HRI), Theory of Mind (ToM) is intended to serve as a user-friendly backend to the interface of robotic systems, enabling robots to in…
LLM-based Interactive Imitation Learning for Robotic Manipulation
Jonas Werner, Kun Chu, Cornelius Weber +1
Recent advancements in machine learning provide methods to train autonomous agents capable of handling the increasing complexity of sequential decision-making in robotics. Imitatio…
LLM+MAP: Bimanual Robot Task Planning using Large Language Models and Planning Domain Definition Language
Kun Chu, Xufeng Zhao, Cornelius Weber +1
Bimanual robotic manipulation provides significant versatility, but also presents an inherent challenge due to the complexity involved in the spatial and temporal coordination betw…
QT-TDM: Planning With Transformer Dynamics Model and Autoregressive Q-Learning
Mostafa Kotb, Cornelius Weber, Muhammad Burhan Hafez +1
Inspired by the success of the Transformer architecture in natural language processing and computer vision, we investigate the use of Transformers in Reinforcement Learning (RL), s…
Agentic Skill Discovery
Xufeng Zhao, Cornelius Weber, Stefan Wermter
Language-conditioned robotic skills make it possible to apply the high-level reasoning of Large Language Models (LLMs) to low-level robotic control. A remaining challenge is to acq…
Large Language Models for Orchestrating Bimanual Robots
Kun Chu, Xufeng Zhao, Cornelius Weber +3
Although there has been rapid progress in endowing robots with the ability to solve complex manipulation tasks, generating control policies for bimanual robots to solve tasks invol…
Accelerating Reinforcement Learning of Robotic Manipulations via Feedback from Large Language Models
Kun Chu, Xufeng Zhao, Cornelius Weber +2
Reinforcement Learning (RL) plays an important role in the robotic manipulation domain since it allows self-learning from trial-and-error interactions with the environment. Still,…
Continual Robot Learning using Self-Supervised Task Inference
Muhammad Burhan Hafez, Stefan Wermter
Endowing robots with the human ability to learn a growing set of skills over the course of a lifetime as opposed to mastering single tasks is an open problem in robot learning. Whi…