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In this work we focus on the collision avoidance problem with respect to a moving spherical object. We assume knowledge of a nominal controller that achieves tracking of a desired trajectory in the absence of obstacles, and we design an auxiliary control scheme to guarantee that the robot&#39;s end-effector will always operate in a safe distance from the moving obstacle&#39;s surface. The controller we develop does not take into account the actual robot dynamics, thus constituting a truly model-free approach.

IEEE RO-MAN 2021

Influences of Media Literacy and Experiences of Robots into Negative Attitudes toward Robots in Japan

collision avoidance

robotic navigation

motion planning

obstacle avoidance

The IFAC Conference on Nonlinear Model Predictive Control (NMPC 2021) aims to bring together researchers interested in and work in the MPC field from both academia and industry. This allows us to reflect and establish the current state-of-the-art and focus on the future development of the MPC field towards relevant directions.

Model predictive control (MPC) is indisputably one of the advanced control techniques that have significantly affected control engineering practice with thousands of controllers implemented in various fields, spanning from the process industry to automotive and robotics. This is due to its unique ability to handle constraints, take preview information about disturbances and setpoint changes into account, and optimize performance. Furthermore, MPC allows the consideration of various types of dynamics, from linear to nonlinear, discrete to continuous, as well as an online adaptation of models and parameters. The research community on MPC is very active, with hundreds of papers published every year. Over the past decades, many theoretical, as well as implementation results, have been achieved.

### **All content presented at NMPC2021, including videos, papers and slides will be available only to registered (regular/student) participants. The content can be accessed until August 31st, 2021.**



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Nonlinear Model Predictive Control Conference 2021

The IFAC Conference on Nonlinear Model Predictive Control (NMPC 2021) aims to bring together researchers interested in and work in the MPC field from both academia and industry. This allows us to reflect and establish the current state-of-the-art and focus on the future development of the MPC field towards relevant directions.

In this work we focus on the collision avoidance problem with respect to a moving spherical object. We assume knowledge of a nominal controller that achieves tracking of a desired trajectory in the absence of obstacles, and we design an auxiliary control scheme to guarantee that the robot's end-effector will always operate in a safe distance from the moving obstacle's surface. The controller we develop does not take into account the actual robot dynamics, thus constituting a truly model-free approach.

panel

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. This theme also touches upon people's expectations and perceptions of robots, as well as other social, cognitive and cultural factors that will influence human-robot interactions. We will have a special session/track on this topic, invite targeted submissions and include plenary speakers who are specialist in this area. We sincerely hope that this focus will help enlighten the design-thinking of all of us as we fabricate and program our own various systems.

To access RO-MAN Virtual 2021, [please register here.](https://ro-man2021.org/registration/)

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. 

Social behaviors and displays of affect are critical for successful child-robot interactions. In this paper we discuss a new approach to creating generalized displays of affect for an appendage from a collection of cartoons. We then discuss the design of a continuum robotic appendage that can display the emotional configurations from our data and evaluation study.

Design and Evaluation of an Affective, Continuum Robotic Appendage for Child-Robot Interaction

In this work we investigated the effect of eight different robot characteristics on an interaction with a child with Type 1 Diabetes, aiming to improve their self-management. Results indicated that movements of the robot were considered entertaining, emotional expressions of the robot contributed to bonding and eye contact was not appreciated by all children. The results can be used to improve interactions between children and robots.

Investigating Experiences with a Robot Teaching Children Self-Management: A Field Trial

This study introduces the control architecture of a platform aimed at promoting good mental health for workers interacting with cobots through automatic adaption capabilities. Starting from the identification of the parameters that could influence the collaboration experience, the envisioned human-driven control structure is presented together with a detailed description of the components required to implement such an automated system.

Relevant Perception Modalities for Flexible Human-Robot Teams

This paper explores Augmented Reality (AR)-based implementations in robotics and categorizes them based on the type of the used device, with the main focus on the least explored category - mobile AR. To determine the user’s experience in robot programming, an AR application prototype is developed as a co-interface to a Teach Pendant. The obtained results detail the users potential benefits from such mobile-based AR solutions in light of the current technologies.

A Study of Cobot Practitioners Needs for Augmented Reality Interfaces in the Context of Current Technologies

In this work, the problem of cooperative human-robot manipulation of an object
with large inertia is addressed. A variable admittance control scheme is proposed,
where the damping is adjusted based on the power transmitted from the human to
the robot. The aim is to minimize the energy injected by the human while also
allowing her/him to have control over the task. Evaluation via human-in-the-loop
simulations and comparison with a generic variable damping state-of-the-art
method is performed.

ConCodeIt! A Comparison of Concurrency Interfaces in Block-Based Visual Robot Programming

To resolve the lack of prosthesis supply and reduce cost issues, this paper provides an overview of a self-trainable user-customized system architecture for a 3D printed prosthetic hand to minimize the challenge of accessing and maintaining these supporting devices.

Would you Take Advice from a Robot? Developing a Framework for Inferring Human-Robot Trust in Time-Sensitive Scenarios

In this paper we explore the utility, usability, and accessibility of a tele-operated Stretch robot in a real kitchen environment. Our focus is on accessible teleoperation -- making it possible for diverse users to control the Stretch to do useful things, both through interface design principles, customization, and by moving towards mixed autonomy

Natural Gradient Shared Control

The ability to accurately identify user intention is a pre-requisite for designing collaborative control schemes to provide appropriate navigational assistance to PAPAW users. In this paper, we investigated a personalized multi-step intention recognition pipeline that consists of probabilistic models for the purpose of clustering, classifying, and filtering user-pushrim interactions. The developed pipeline is computationally efficient and was successfully implemented and tested in real-time. This framework can provide a foundation for the future development of a context-aware controller for PAPAWs by improving their controllability and maneuverability.

Living-Lab and Experimental Workshops for Design of I-RobEka Assistive Shopping Robot: ELSI Aspects with MEESTAR {303}

In-The-Wild learning from demonstration for therapies for autisim spectrum disorder. We developed a learning from demonstration based model to identify therapists' verbal behavior, and learn a generative model to output corresponding verbal behaviour based on the child's audio spectrogram input.

Human-Robot Artistic Co-Creation: a Study in Improvised Robot Dance

We present a AR-based robot visualization system which allows people to visualize robots and their intent, allowing users to see robots through walls and gain greater awareness of robots in shared human-robot workspaces.

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Influences of Media Literacy and Experiences of Robots into Negative Attitudes toward Robots in Japan

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Design and Evaluation of an Affective, Continuum Robotic Appendage for Child-Robot Interaction

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