United States

With increased interest in close-proximity human-robot collaboration in production settings it is important that we understand how robot behaviors and mistakes affect human-robot trust. We designed a system for real-time human-robot trust assessment using a top-down depth camera tracking setup with the goal of using signs of physical apprehension to infer decreases in trust toward the robot. In an experiment with 20 participants we evaluated the tracking system in a repetitive collaborative pick-and-place task with a Rethink Robotics Sawyer robot.

IEEE RO-MAN 2021

Interviewing Style for a Social Robot

body tracking

laboratory experiment

human-robot trust

human-robot collaboration

human-robot interaction

computer vision

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. 

panel

Abstract— Today’s world is undeniably technology-driven and children are the ones who adopt technology with ease. This fact could be leveraged to design assistive technologies for children using social robots as they are observed to be efficient pedagogical agents. Robotic technology is evolving rapidly and robots are designed to play social roles in education, health care and home assistance. However, there has been limited research focusing on the use of robotic technologies for designing interactions with children in the global south, owing to which the response behaviour towards robot-assisted interventions is unknown. To address this gap, we conducted a study to understand the response behaviour of Indian children
of the age 3-6 years towards robot-assisted interventions during directive tasks. Our analysis shows that the children could follow to robot’s instructions during the tasks and complete the tasks successfully. The exploratory outcomes also highlight
the acceptance and benefits of using robotic assistants as a facilitator in education, cognitive therapies and healthcare.

Understanding the Perception of Incremental Robot Response in Human-Robot Interaction

With the increased use of social robots in critical
applications, like elder care and rehabilitation, it becomes necessary
to investigate the user’s trust in robots to prevent over- and
under-utilization of the robotic systems. While several studies
have shown how trust increases through personalised behaviour,
there is a lack of research concerned with the influence of
personalised physical appearance. This study explores the effect
of personalised physical appearance on trust in human-robotinteraction
(HRI).

The Difference between Trust Measurement and Behavior: Investigating the Effect of Personalizing a Robot's Appearance on Trust in HRI

We used the framing effect to examine whether people subjectively percieve robots in a human-robot collaborative environment

Adaptive impedance control with trajectory adaptation for minimizing interaction force

Robots create a window of opportunity to challenge the barriers to play that children with special needs face. We created a new robot, MyJay, to address this need and involved various stakeholders in the project.

Design, development, and preliminary evaluation of a highly wearable

We propose a CNN-LSTM network to estimate human engagement intention.
We build a multi-branch, inputs-adjustable architecture to provide the flexibility of accommodating different types of input features (image features and semantic social cues).
We propose a feature transition method to handle multiparty HRI.

Engagement Intention Estimation in Multiparty Human-Robot Interaction

We address the Object Goal Navigation problem, which tasks the agent with searching an instance of a goal category. Rather than developing goal-driven exploration policies, we aim to adapt the existing exploration policies that maximize scene coverage to be goal-conditioned. Thus, we propose a standalone scene understanding module to identify potential regions where the goal occurs. We also propose Goal-Conditioned Exploration (GCExp), an algorithm that entails the integration of our novel scene understanding module with any existing exploration policy.

How Can a Robot Trigger Human Backchanneling?

3D avatars are widely used in various fields of emerging technology, from augmented reality to social robots. To interact in a natural way with the user, they must be able to show at least some basic emotions. However, generating animation for these virtual avatars is a time-consuming task and a creative process. The main goal of this work is to facilitate a generation of facial animation of basic emotions on a 3D avatar. To this end, we developed and compared two approaches. The first method consists of the generation of animation using tuning Blendshape features of the 3D model, whereas the second method captures it from the real face and maps it on the model correspondingly. Additionally, the text, from which the emotion was estimated, was passed to lip synchronization software for generating realistic lip movements for the avatar. Then, animations of six basic emotions were shown in different variations in the survey and respondents were asked to guess the emotion shown in the video. Besides, such anthropomorphic features of the avatar as human-likeness, life-likeness and pleasantness were examined. In general, the analysis of the survey provided the following interesting findings: a) participants did not have significant differences in recognizing emotions based on the type of animation generation method; b) inclusion of voice significantly enhanced the recognition of emotion. In relation to participants' accuracy of emotion recognition, Excitement and Happiness were mostly confused between each other more than any other two emotions, while Anger was the easiest emotion to recognize.


Facial Expression Generation of 3D Avatar Based on Semantic Analysis

This work proposes a path re-planning strategy combined with a speed scaling technique for human-robot collaboration. The re-planning algorithm exploits a set of pre-computed paths to find a new path when the current one is obstructed by the operator, or to optimize the current trajectory otherwise. The re-planner allows reducing the need for speed reductions and safety stops. Compared to the common three-fixed-areas speed and separation monitoring approach, it offers a safe, more efficient, and natural collaboration.

Hatsuki: An anime character like robot figure platform with anime-style expressions and imitation learning based action generation

This work proposes a modular system for agile production using a marker less camera-based system using real-time joint position estimations as input and providing a continuous or summarized individual ergonomic feedback as output. First implementation results considering the requirements and overall system design for online ergonomic evaluation in agile production environment are discussed.

Modular System Design Approach for Online Ergonomics Assessment in Agile Production Environment

For an effective deployment in manufacturing,
Collaborative Robots should be capable of adapting their
behavior to the state of the environment and to keep the user
safe and engaged during the interaction. Artificial Intelligence
(AI) enables robots to autonomously operate understanding
the environment, planning their tasks and acting to achieve
some given goals. However, the effective deployment of AI technologies
in real industrial environments is not straightforward.
There is a need for engineering tools facilitating communication
and interaction between AI engineers and Domain experts. This
paper proposes a novel software tool, called TENANT (Tool
fostEriNg Ai plaNning in roboTics) whose aim is to facilitate
the use of AI planning technologies by providing domain
experts like e.g., production engineers, with a graphical software
framework to synthesize AI planning models abstracting from
syntactic features of the underlying planning formalism.

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