Luka Peternel — accepted AI-conference papers

2026

Enabling Embodied Human-Robot Co-Learning: Requirements, Method, and Test with Handover Task

Emma M. van Zoelen, Hugo Veldman-Loopik, Karel van den Bosch, Mark Neerincx, David A. Abbink, Luka Peternel

ICRA 2026poster

Despite a large body of research on robot learning, it has not yet been thoroughly studied how collaborating humans and robots learn reciprocally. In such situations, both humans and robots continuously learn about each other and the task through interaction. This paper addresses the research questi…

Cited by 0 Source ↗Scholar ↗

2026

Fitts' List Revisited: An Empirical Study on Function Allocation in a Two-Agent Physical Human-Robot Collaborative Position/Force Task

Nicky Mol, Joseph Micah Prendergast, David A. Abbink, Luka Peternel

RA-L 2026

In this letter, we investigate whether classical function allocation—the principle of assigning tasks to either a human or a machine—holds for physical Human-Robot Collaboration, which is important for providing insights for Industry 5.0 to guide how to best augment rather than replace workers. This

Cited by 2 Source ↗Scholar ↗

2026

Fitts' List Revisited: An Empirical Study on Function Allocation in a Two-Agent Physical Human-Robot Collaborative Position/Force Task

Nicky Mol, J. Micah Prendergast, David A. Abbink, Luka Peternel

ICRA 2026poster

In this letter, we investigate whether classical function allocation--the principle of assigning tasks to either a human or a machine--holds for physical Human-Robot Collaboration, which is important for providing insights for Industry 5.0 to guide how to best augment rather than replace workers. Th…

Cited by 0 Source ↗PDF ↗Scholar ↗

2025

Enabling Embodied Human-Robot Co-Learning: Requirements, Method, and Test With Handover Task

Emma M. van Zoelen, Hugo Veldman-Loopik, Karel van den Bosch, Mark A. Neerincx, David A. Abbink, Luka Peternel

RA-L 2025

Cited by 3 Source ↗Scholar ↗

2025

Planning Human-Robot Co-Manipulation With Human Motor Control Objectives and Multi-Component Reaching Strategies

Kevin Haninger, Luka Peternel

RA-L 2025

For successful goal-directed human-robot interaction, the robot should adapt to the intentions and actions of the collaborating human. This can be supported by musculoskeletal or data-driven human models, where the former are limited to lower-level functioning such as ergonomics, and the latter have

Cited by 10 Source ↗Scholar ↗

2025

Teleoperated Teaching of Task and Impedance (TTTI): Multi-Modal Interface Extending Haptic Device for Robotic Skill Transfer

Astrid W. E. Rots, Luka Peternel

IROS 2025

In this paper, we propose a concept of Teleoperated Teaching of Task and Impedance (TTTI) with a novel multi-modal interface that enables online teleoperated teaching of combined low-level impedance-regulation skills and high-level task decision-making skills using a single hand-held haptic device.

Cited by 0 Source ↗Scholar ↗

2024

Creating Discomfort Maps via Hand-held Human Feedback Interface for Robotic Shoulder Physiotherapy

Jevon Ravenberg, Italo Belli, J. Micah Prendergast, Ajay Seth, Luka Peternel

IROS 2024poster

In this work, we propose a method of capturing the patient’s discomfort during robotic shoulder physiotherapy, creating "discomfort maps". These maps depict the personalized distribution of discomfort that each patient perceived across their shoulder range of motion, facilitating both robotic device…

Cited by 0 Source ↗Scholar ↗

2024

Interactive Multi-Stiffness Mixed Reality Interface: Controlling and Visualizing Robot and Environment Stiffness

Alejandro Díaz Rosales, Jose Rodriguez-Nogueira, Eloise Matheson, David A. Abbink, Luka Peternel

IROS 2024

Teleoperation is a crucial technology enabling human operators to control robots remotely to perform tasks in hazardous and difficult-to-access environments. Tasks in such environments often involve complex physical interactions with tools and objects of various softness. To this end, teleimpedance

Cited by 3 Source ↗Scholar ↗

2023

Human Modeling in Physical Human-Robot Interaction: A Brief Survey

Cheng Fang, Luka Peternel, Ajay Seth, Massimo Sartori, Katja D. Mombaur, Eiichi Yoshida

RA-L 2023

The advancement and development of human modeling have greatly benefited from principles used in robotics, for instance, multibody dynamics laid the foundations for physics engines of human movement simulation, and the robotics and control theory were used to contextualize human sensorimotor control

Cited by 29 Source ↗Scholar ↗

2023

Orbital Head-Mounted Display: A Novel Interface for Viewpoint Control during Robot Teleoperation in Cluttered Environments

Sjoerd Kuitert, Jelle Hofland, Cock J. M. Heemskerk, David A. Abbink, Luka Peternel

IROS 2023poster

Robotic teleoperation is used in various applications, including the nuclear industry, where the experience and intelligence of a human operator are necessary for making complex decisions that are beyond the autonomy of robots. Human-robot interfaces that help strengthen an operators situational awa…

Cited by 2 Source ↗Scholar ↗

2023

Robotic Skill Mutation in Robot-to-Robot Propagation During a Physically Collaborative Sawing Task

Rosa E. S. Maessen, Joseph Micah Prendergast, Luka Peternel

RA-L 2023

Skill propagation among robots without human involvement can be crucial in quickly spreading new physical skills to many robots. In this respect, it is a good alternative to pure reinforcement learning, which can be time-consuming, or learning from human demonstration, which requires human involveme

Cited by 2 Source ↗Scholar ↗

2022

Foot-operated Tele-impedance Interface for Robot Manipulation Tasks in Interaction with Unpredictable Environments

Stijn Klevering, Winfred Mugge, David A. Abbink, Luka Peternel

IROS 2022poster

Tele-impedance increases interaction performance between a robotic tool and unstructured/unpredictable en-vironments during teleoperation. However, the existing tele-impedance interfaces have several ongoing issues, such as long calibration times and various obstructions for the human operator. In a…

Cited by 5 Source ↗Scholar ↗

2022

Model Predictive Control with Gaussian Processes for Flexible Multi-Modal Physical Human Robot Interaction

Kevin Haninger, Christian Hegeler, Luka Peternel

ICRA 2022poster

Physical human-robot interaction can improve human ergonomics, task efficiency, and the flexibility of automation, but often requires application-specific methods to detect human state and determine robot response. At the same time, many potential human-robot interaction tasks involve discrete modes…

Cited by 34 Source ↗Scholar ↗

2021

Biomechanics Aware Collaborative Robot System for Delivery of Safe Physical Therapy in Shoulder Rehabilitation

Joseph Micah Prendergast, Stephan Balvert, Tom Driessen, Ajay Seth, Luka Peternel

RA-L 2021

In this work, we explore using computational musculoskeletal modeling to equip an industrial collaborative robot with awareness of the internal state of a patient to safely deliver physical therapy. A major concern of robot-mediated physical therapy is that robots may unwittingly injure patients. Fo

Cited by 27 Source ↗Scholar ↗

2021

ILoSA: Interactive Learning of Stiffness and Attractors

Giovanni Franzese, Anna Mészáros, Luka Peternel, Jens Kober

IROS 2021poster

Teaching robots how to apply forces according to our preferences is still an open challenge that has to be tackled from multiple engineering perspectives. This paper studies how to learn variable impedance policies where both the Cartesian stiffness and the attractor can be learned from human demons…

Cited by 36 Source ↗code ↗Scholar ↗

2019

A Capability-Aware Role Allocation Approach to Industrial Assembly Tasks

Edoardo Lamon, Alessandro De Franco, Luka Peternel, Arash Ajoudani

RA-L 2019

The deployment of industrial robotic cells based on lean manufacturing principles enables the development of fast-reconfigurable assembly lines in which human and robotic agents collaborate to achieve a shared task. To ensure the effective coordination of the shared effort, each task must be decompo

Cited by 64 Source ↗Scholar ↗

2018

A Method for Robot Motor Fatigue Management in Physical Interaction and Human-Robot Collaboration Tasks

Luka Peternel, Nikos Tsagarakis, Arash Ajoudani

IROS 2018poster

Collaborative robots are often designed with limited power and force capacity, with the aim to provide affordable solutions and ensure human safety in case of accidental collisions and impacts. If a task requires a power beyond this capacity, or is performed repeatedly over long periods, such limits…

Cited by 5 Source ↗Scholar ↗

2018

Anticipatory Robot Assistance for the Prevention of Human Static Joint Overloading in Human-Robot Collaboration

Wansoo Kim, Jinoh Lee, Luka Peternel, Nikolaos G. Tsagarakis, Arash Ajoudani

RA-L 2018

This letter proposes a novel human–robot collaboration (HRC) control approach to alert and reduce the static joint torque overloading of a human partner while executing shared tasks with a robot. Using a preidentified statically equivalent serial chain model, variations of the centre-of-pressure and

Cited by 113 Source ↗Scholar ↗

2018

Online Human Muscle Force Estimation for Fatigue Management in Human-Robot Co-Manipulation

Luka Peternel, Cheng Fang, Nikos Tsagarakis, Arash Ajoudani

IROS 2018poster

In this paper, we propose a novel method for selective management of muscle fatigue in human-robot co-manipulation. The proposed framework enables the detection of excessive fatigue levels of an individual muscle group while executing a certain task, and provides anticipatory robotic responses to di…

Cited by 32 Source ↗Scholar ↗

2017

A Method for Derivation of Robot Task-Frame Control Authority from Repeated Sensory Observations

Luka Peternel, Leonel Dario Rozo, Darwin G. Caldwell, Arash Ajoudani

RA-L 2017

In this letter, we propose a novel method that enables the robot to autonomously devise an appropriate control strategy from human demonstrations without a prior knowledge of the demonstrated task. The method is primarily based on observing the patterns and consistency in the observed dataset. This

Cited by 24 Source ↗Scholar ↗

2017

Power-augmentation control approach for arm exoskeleton based on human muscular manipulability

Rok Goljat, Jan Babič, Tadej Petrič, Luka Peternel, Jun Morimoto

ICRA 2017poster

The paper presents a novel control method for the arm exoskeletons that takes into account the muscular force manipulability of the human arm. In contrast to classical controllers that provide assistance without considering the biomechanical properties of the human arm, we propose a control method t…

Cited by 19 Source ↗Scholar ↗

2016

A shared control method for online human-in-the-loop robot learning based on Locally Weighted Regression

Luka Peternel, Erhan Oztop, Jan Babič

IROS 2016poster

We propose a novel method that arbitrates the control between the human and the robot actors in a teaching-by-demonstration setting to form synergy between the two and facilitate effective skill synthesis on the robot. We employed the human-in-the-loop teaching paradigm to teleoperate and demonstrat…

Cited by 29 Source ↗Scholar ↗

2016

Towards multi-modal intention interfaces for human-robot co-manipulation

Luka Peternel, Nikos Tsagarakis, Arash Ajoudani

IROS 2016poster

This paper presents a novel approach for human-robot cooperation in tasks with dynamic uncertainties. The essential element of the proposed method is a multi-modal interface that provides the robot with the feedback about the human motor behaviour in real-time. The human muscle activity measurements…

Cited by 96 Source ↗Scholar ↗

2015

Human-in-the-loop approach for teaching robot assembly tasks using impedance control interface

Luka Peternel, Tadej Petrič, Jan Babič

ICRA 2015poster

In this paper we propose a human-in-the-loop approach for teaching robots how to solve part assembly tasks. In the proposed setup the human tutor controls the robot through a haptic interface and a hand-held impedance control interface. The impedance control interface is based on a linear spring-ret…

Cited by 95 Source ↗Scholar ↗