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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&#39; 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.


IEEE RO-MAN 2021

Facial Expression Generation of 3D Avatar Based on Semantic Analysis

facial expression generation

virtual avatars

semantic analysis

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

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.


technical paper

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. 

A socially assistive robot can support children in developmental and well-being aspects. The robot's visual aesthetics influences users' initial judgments and is essential for creating first impressions, trust, and desirability. Our work proposes and demonstrates a methodology for evaluating children's perception of visual qualities in SARs, aiming to pave the way for new design tools to support robotic designers and developers.

Overhead Work Assist with Passive Gravity Compensation Mechanism and Horizontal Link Mechanism for Agriculture

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

This work presents the improvements of Hydracer, a robot manipulator driven by electro-hydrostatic actuators, to achieve human-robot interaction. To solve the problems of Hydracer and to achieve flexible force control, we worked on the system improvements: reduction of internal leakage by considering oil property, enhancement of reliability of ceramics components, and the parameter identification to improve its controllability. Flexible force control of Hydracer by zero-torque control with gravity compensation was realized.


TIRL: Enriching Actor-Critic RL with non-expert human teachers\\ and a Trust Model

We implemented a dedicated human-aware navigation system that adapts the configuration of the navigation parameters based on two human-like personalities, extrovert and introvert, and we compared them with a no social behavior. We evaluated the system in a dynamic scenario in which each participant needed to pass by a robot moving in the opposite direction, showing a different personality each time. The results show that 19 out of 20 subjects involved in the study perceived a difference among the personalities exhibited by the robot, both in terms of proxemics and velocity. Furthermore, the results highlight a general preference of a complementary robot’s personality, helping to suggest some guidelines for future works in the human-aware navigation field.

Modeling Human-Like Robot Personalities As a Key to Foster Socially Aware Navigation

While we've made signfiicant progress in models of gesture production, there are still significant behavioral effects on humans that observe these gestures. Namely, we find that f-formation, perspective taking, feasability in the configuration space, and exophora play outsized roles in present day gesture generation models for robots. We suggest a way forward but leave it to future work to explore.

Evaluation of Immersive Teleoperation Systems using Standardized Tasks and Measurements

The identification of social groups has been a challenge for robotics as it encompasses a number of disciplines. We propose a hierarchical clustering method for grouping individuals into free standing conversational groups, utilising their position and orientation. The proposed method is evaluated on the SALSA dataset with achieved F1 score of 0.94. The algorithm is also evaluated for scalability and implemented on a mobile robot attempting to detect social groups and engage in interaction.

Designing Robotic Cabinets That Assist Users' Tyding Behaviors

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.

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Facial Expression Generation of 3D Avatar Based on Semantic Analysis

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