72 citations · 359 across the 23 of their papers we have counts for
24 papers
Learning Deep Dynamical Systems using Stable Neural ODEs
Andreas Sochopoulos, Michael Gienger, Sethu Vijayakumar
Learning complex trajectories from demonstrations in robotic tasks has been effectively addressed through the utilization of Dynamical Systems (DS). State-of-the-art DS learning me…
Impact-Aware Bimanual Catching of Large-Momentum Objects
Lei Yan, Theodoros Stouraitis, João Moura +3
This paper investigates one of the most challenging tasks in dynamic manipulation -- catching large-momentum moving objects. Beyond the realm of quasi-static manipulation, dealing…
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…
Predictive and Robust Robot Assistance for Sequential Manipulation
Theodoros Stouraitis, Michael Gienger
This paper presents a novel concept to support physically impaired humans in daily object manipulation tasks with a robot. Given a user's manipulation sequence, we propose a predic…
Communicating Robot's Intentions while Assisting Users via Augmented Reality
Chao Wang, Theodoros Stouraitis, Anna Belardinelli +2
This paper explores the challenges faced by assistive robots in effectively cooperating with humans, requiring them to anticipate human behavior, predict their actions' impact, and…
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…
A Glimpse in ChatGPT Capabilities and its impact for AI research
Frank Joublin, Antonello Ceravola, Joerg Deigmoeller +3
Large language models (LLMs) have recently become a popular topic in the field of Artificial Intelligence (AI) research, with companies such as Google, Amazon, Facebook, Amazon, Te…
Understanding the Uncertainty Loop of Human-Robot Interaction
Jan Leusmann, Chao Wang, Michael Gienger +2
Recently the field of Human-Robot Interaction gained popularity, due to the wide range of possibilities of how robots can support humans during daily tasks. One form of supportive…
Learning from Few Demonstrations with Frame-Weighted Motion Generation
Jianyong Sun, Jens Kober, Michael Gienger +1
Learning from Demonstration (LfD) enables robots to acquire versatile skills by learning motion policies from human demonstrations. It endows users with an intuitive interface to t…
Robotic Fabric Flattening with Wrinkle Direction Detection
Yulei Qiu, Jihong Zhu, Cosimo Della Santina +2
Deformable Object Manipulation (DOM) is an important field of research as it contributes to practical tasks such as automatic cloth handling, cable routing, surgical operation, etc…
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…
Do You Need a Hand? -- a Bimanual Robotic Dressing Assistance Scheme
Jihong Zhu, Michael Gienger, Giovanni Franzese +1
Developing physically assistive robots capable of dressing assistance has the potential to significantly improve the lives of the elderly and disabled population. However, most rob…
ROS-PyBullet Interface: A Framework for Reliable Contact Simulation and Human-Robot Interaction
Christopher E. Mower, Theodoros Stouraitis, João Moura +7
Reliable contact simulation plays a key role in the development of (semi-)autonomous robots, especially when dealing with contact-rich manipulation scenarios, an active robotics re…
Learning Task-Parameterized Skills from Few Demonstrations
Jihong Zhu, Michael Gienger, Jens Kober
Moving away from repetitive tasks, robots nowadays demand versatile skills that adapt to different situations. Task-parameterized learning improves the generalization of motion pol…
Distilled Domain Randomization
Julien Brosseit, Benedikt Hahner, Fabio Muratore +2
Deep reinforcement learning is an effective tool to learn robot control policies from scratch. However, these methods are notorious for the enormous amount of required training dat…
Robot Learning from Randomized Simulations: A Review
Fabio Muratore, Fabio Ramos, Greg Turk +3
The rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require large amounts of data. Unfortunately, it is prohibitively expensive to gen…
Set-based State Estimation with Probabilistic Consistency Guarantee under Epistemic Uncertainty
Shen Li, Theodoros Stouraitis, Michael Gienger +2
Consistent state estimation is challenging, especially under the epistemic uncertainties arising from learned (nonlinear) dynamic and observation models. In this work, we propose a…
Challenges and Outlook in Robotic Manipulation of Deformable Objects
Jihong Zhu, Andrea Cherubini, Claire Dune +10
Deformable object manipulation (DOM) is an emerging research problem in robotics. The ability to manipulate deformable objects endows robots with higher autonomy and promises new a…
Multi-mode Trajectory Optimization for Impact-aware Manipulation
Theodoros Stouraitis, Lei Yan, João Moura +2
The transition from free motion to contact is a challenging problem in robotics, in part due to its hybrid nature. Additionally, disregarding the effects of impacts at the motion p…
Data-efficient Domain Randomization with Bayesian Optimization
Fabio Muratore, Christian Eilers, Michael Gienger +1
When learning policies for robot control, the required real-world data is typically prohibitively expensive to acquire, so learning in simulation is a popular strategy. Unfortunate…
Assessing Transferability from Simulation to Reality for Reinforcement Learning
Fabio Muratore, Michael Gienger, Jan Peters
Learning robot control policies from physics simulations is of great interest to the robotics community as it may render the learning process faster, cheaper, and safer by alleviat…
Monitoring in the Clouds: Comparison of ECO2Clouds and EXCESS Monitoring Approaches
Pavel Skvortsov, Dennis Hoppe, Axel Tenschert +1
With the increasing adoption of private cloud infrastructures by providers and enterprises, the monitoring of these infrastructures is becoming crucial. The rationale behind monito…