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We introduce a Grasp Manifold Estimator (GraspME) to find object&#39;s grasp affordances directly in camera images. Predicting grasp manifolds has the advantage of generalizing to continuously infinitely many grasps, which is not the case when using other grasp representations. We approximate grasp manifolds via a set of keypoints and locate them in images using Mask R-CNN as a backbone. We rely on simulation data only and perform experiments on simple and complex objects, including unseen ones and real data.

IEEE RO-MAN 2021

GraspME - Grasp Manifold Estimator

grasping

perception

detection

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

We introduce a Grasp Manifold Estimator (GraspME) to find object's grasp affordances directly in camera images. Predicting grasp manifolds has the advantage of generalizing to continuously infinitely many grasps, which is not the case when using other grasp representations. We approximate grasp manifolds via a set of keypoints and locate them in images using Mask R-CNN as a backbone. We rely on simulation data only and perform experiments on simple and complex objects, including unseen ones and real data.

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. 

Hand disinfection is important in preventing infectious diseases. However not everyone sanitizes their hands, and many people don’t rub hands for a long enough time. For these reasons we designed a robotic interface for automatic hand sanitizer dispensers to motivate people to disinfect their hands better. In our in-the-wild studies we have found that using our system resulted in 21% more hand sanitizations and a much longer hand rubbing time, which leads to better hygiene and prevention of infectious diseases.

Integrating a Social Robot in Higher Education – A Field Study

This trial involved an automated social robot in a health clinic to collect health data and provide personalized feedback on four key factors: exercise, diet, alcohol and cigarette use. A total of 47 patients completed a 20-minute health questionnaire with a Pepper Robot. Patients reported being as comfortable to discuss health-related topics with a robot or human for exercise, diet, alcohol, cigarette use, and mental health. Program evaluation received moderate ratings for ease of use, usefulness and motivation to change.

The Effects of Robot’s Facial Expressions on Children’s First Impressions of Trustworthiness

The paper describes the Nao_PRM application aimed at expanding the limited functionality of the simulated Nao robot, already integrated into the Choregraphe application, creating a robot simulation with all the required functionalities for social interaction.

Efficient, Situated and Ontology based Referring Expression Generation for Human-Robot collaboration

This project relates to developing a framework for physiologically adaptive robotic scenarios. By exploring the designs of previously developed systems, we can better provide a general structure and multi-purpose tools to help facilitate the integration of physiological signals into HRI applications.

Hand-Motion-guided Articulation and Segmentation Estimation

Studies have shown that the appearance and movements of home robots may play key roles in the impression and engagement of users, opposed to recent rises in the smart speaker market. In this research, we conduct a user experiment with the aim of clarifying the elements required to evaluate the human impression of a robot's movements, based on the hypothesis that adequate movements may lead to better impressions and engagement.

Impression Evaluation of Robots Behavior When Assisting Human in a Cooking Task

We explored using human cognitive traits as predictive measures for human-robot team task performance. We developed several tests that quantify human cognitive traits, and analyzed the relationships between the test scores and performances on several human-robot team tasks.

Predicting Individual Human Performance in Human-Robot Teaming

We developed a new rule-based dialog system, which appropriately switches responses without interfering with human speech. The traditional rule-based part allows the system user to design and customize topics and conversation transitions easily. However, the system’s speech sometimes superimposes on human’s speech. To solve this problem, we installed turn-taking detection, which can detect the human speaker’s continuous speech, and a function to select response by using detection results. Practical experiment suggested that our system may enable smooth conversations.

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