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Michael Gienger

25 accepted 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…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

Bimanual Robot-Assisted Dressing: A Spherical Coordinate-Based Strategy for Tight-Fitting Garments

Jian Zhao, Yunlong Lian, Andy M. Tyrrell, Michael Gienger, Jihong Zhu
IROS 2025

Robot-assisted dressing is a popular but challenging topic in the field of robotic manipulation, offering significant potential to improve the quality of life for individuals with mobility limitations. Currently, the majority of research on robot-assisted dressing focuses on how to put on loose-fitt

Cited by 0Source ↗Scholar ↗
2025

CCDP: Composition of Conditional Diffusion Policies with Guided Sampling

Amirreza Razmjoo, Sylvain Calinon, Michael Gienger, Fan Zhang
IROS 2025

Imitation Learning offers a promising approach to learn directly from data without requiring explicit models, simulations, or detailed task definitions. During inference, actions are sampled from the learned distribution and executed on the robot. However, sampled actions may fail for various reason

Cited by 2Source ↗code ↗Scholar ↗
2025

Fast Flow-based Visuomotor Policies via Conditional Optimal Transport Couplings

Andreas Sochopoulos, Nikolay Malkin, Nikolaos Tsagkas, Joao Moura, Michael Gienger, Sethu Vijayakumar
CoRL 2025poster

Diffusion and flow matching policies have recently demonstrated remarkable performance in robotic applications by accurately capturing multimodal robot trajectory distributions. However, their computationally expensive inference, due to the numerical integration of an ODE or SDE, limits their applic…

Cited by 0Source ↗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 51Source ↗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 16Source ↗Scholar ↗
2023

Learning Personalised Human Sit-to-Stand Motion Strategies via Inverse Musculoskeletal Optimal Control

Daniel F. N. Gordon, Andreas Christou, Theodoros Stouraitis, Michael Gienger, Sethu Vijayakumar
ICRA 2023poster

Physically assistive robots and exoskeletons have great potential to help humans with a wide variety of collaborative tasks. However, a challenging aspect of the control of such devices is to accurately model or predict human behaviour, which can be highly individual and personalised. In this work,…

Cited by 5Source ↗Scholar ↗
2022

ROS-PyBullet Interface: A Framework for Reliable Contact Simulation and Human-Robot Interaction

Christopher Mower, Theodoros Stouraitis, João Moura, Christian Rauch, Lei Yan, Nazanin Zamani Behabadi +4
CoRL 2022poster

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 research topic. Besides simulation, components such as sensing, perception, data collection, robot hardware control, human…

Cited by 22Source ↗code ↗Scholar ↗
2022

Set-Based State Estimation With Probabilistic Consistency Guarantee Under Epistemic Uncertainty

Shen Li, Theodoros Stouraitis, Michael Gienger, Sethu Vijayakumar, Julie A. Shah
RA-L 2022

Consistent state estimation is challenging, especially under the epistemic uncertainties arising from learned (nonlinear) dynamic and observation models. In this work, we propose a set-based estimation algorithm, named Gaussian Process-Zonotopic Kalman Filter (GP-ZKF), that produces zonotopic state

Cited by 12Source ↗Scholar ↗
2021

Neural Posterior Domain Randomization

Fabio Muratore, Theo Gruner, Florian Wiese, Boris Belousov, Michael Gienger, Jan Peters
CoRL 2021poster

Combining domain randomization and reinforcement learning is a widely used approach to obtain control policies that can bridge the gap between simulation and reality. However, existing methods make limiting assumptions on the form of the domain parameter distribution which prevents them from utilizi…

Cited by 46Source ↗Scholar ↗
2020

Multi-mode Trajectory Optimization for Impact-aware Manipulation

Theodoros Stouraitis, Lei Yan, João Moura, Michael Gienger, Sethu Vijayakumar
IROS 2020poster

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 planning level often results in intractable impulsive contact forces. In this paper, we introduce an impact-aware multi-m…

Cited by 23Source ↗Scholar ↗
2020

Predicting and Optimizing Ergonomics in Physical Human-Robot Cooperation Tasks

Linda van der Spaa, Michael Gienger, Tamas Bates, Jens Kober
ICRA 2020poster

This paper presents a method to incorporate ergonomics into the optimization of action sequences for bi-manual human-robot cooperation tasks with continuous physical interaction. Our first contribution is a novel computational model of the human that allows prediction of an ergonomics assessment cor…

Cited by 62Source ↗Scholar ↗
2018

Domain Randomization for Simulation-Based Policy Optimization with Transferability Assessment

Fabio Muratore, Felix Treede, Michael Gienger, Jan Peters
CoRL 2018

Exploration-based reinforcement learning on real robot systems is generally time-intensive and can lead to catastrophic robot failures. Therefore, simulation-based policy search appears to be an appealing alternative. Unfor- tunately, running policy search on a slightly faulty simulator can easily l

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2018

Dyadic collaborative Manipulation through Hybrid Trajectory Optimization

Theodoros Stouraitis, Iordanis Chatzinikolaidis, Michael Gienger, Sethu Vijayakumar
CoRL 2018

This work provides a principled formalism to address the joint planning problem in dyadic collaborative manipulation (DcM) scenarios by representing the human’s intentions as task space forces and solving the joint problem holistically via model-based optimization. The proposed method is the first t

Cited by 0Source ↗PDF ↗Scholar ↗
2018

Human-Robot Cooperative Object Manipulation with Contact Changes

Michael Gienger, Dirk Ruiken, Tamas Bates, Mohamed Regaieg, Michael MeiBner, Jens Kober +2
IROS 2018poster

This paper presents a system for cooperatively manipulating large objects between a human and a robot. This physical interaction system is designed to handle, transport, or manipulate large objects of different shapes in cooperation with a human. Unique points are the bi-manual physical cooperation,…

Cited by 31Source ↗Scholar ↗
2018

Mixture of Attractors: A Novel Movement Primitive Representation for Learning Motor Skills From Demonstrations

Simon Manschitz, Michael Gienger, Jens Kober, Jan Peters
RA-L 2018

In this letter, we introduce Mixture of Attractors, a novel movement primitive representation that allows for learning complex object-relative movements. The movement primitive representation inherently supports multiple coordinate frames, enabling the system to generalize a skill to unseen object p

Cited by 19Source ↗Scholar ↗
2016

Probabilistic decomposition of sequential force interaction tasks into Movement Primitives

Simon Manschitz, Michael Gienger, Jens Kober, Jan Peters
IROS 2016poster

Learning sequential force interaction tasks from kinesthetic demonstrations is a promising approach to transfer human manipulation abilities to a robot. In this paper we propose a novel concept to decompose such demonstrations into a set of Movement Primitives (MPs). The decomposition is based on a…

Cited by 18Source ↗Scholar ↗
2015

Probabilistic progress prediction and sequencing of concurrent movement primitives

Simon Manschitz, Jens Kober, Michael Gienger, Jan Peters
IROS 2015poster

Classical approaches towards learning coordinated movement tasks often represent a movement in a sequential and exclusive fashion. Introducing concurrency allows to decompose such tasks into a number of separate sequences, for instance for two different end-effectors. While this results in a compact…

Cited by 9Source ↗Scholar ↗
2015

Task-dependent distribution and constrained optimization of via-points for smooth robot motions

ChangHyun Sung, Manuel Mühlig, Michael Gienger, Yoji Uno
ICRA 2015poster

This paper presents an effective method for planning and optimizing robot motions in joint space using via-points. The via-point formulation allows for a sparse movement representation which is inherently smooth according to a minimum jerk criterion. In this research we focus on two aspects. First,…

Cited by 4Source ↗Scholar ↗