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We investigate two extensions to our grounded word-learning system to increase transparency. The first extension uses deictic gestures (i.e., pointing) to interact with users. The second extension visualizes the current state of the lexicon on the robot&#39;s tablet. We performed a user study (n=32) to evaluate the system. In a quantitative analysis, we did not see a performance increase for the extensions, but found other benefits. In a qualitative analysis, we identified challenges for word-learning systems.

IEEE RO-MAN 2021

Monocular 3D object detection for an indoor robot environment

grounded language-learning

multimodal interaction

transparency

human-robot interaction

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. 

We investigate two extensions to our grounded word-learning system to increase transparency. The first extension uses deictic gestures (i.e., pointing) to interact with users. The second extension visualizes the current state of the lexicon on the robot's tablet. We performed a user study (n=32) to evaluate the system. In a quantitative analysis, we did not see a performance increase for the extensions, but found other benefits. In a qualitative analysis, we identified challenges for word-learning systems.

technical paper

In this work, we have developed an autonomous robotic assistant for the task of blood suction in robotic surgery. This system was demonstrated to improve surgical performance and reduced cognitive demands compared to the manual teleoperation of the suction. Moreover, the system was tested in a user study. In this, the autonomy collaborated with the user to complete a surgical task. This study allowed us to identify key collaboration aspects required for an effective autonomous blood suction system.

Multi-camera Torso Pose Estimation using Graph Neural Networks

The paper discusses ethical issues in dialogue engagement to build a long-term trusting relationship between a human and a robot agent over a period of time.

Do You Remember Me? Ethical Issues in Long-Term Social Robot Interactions

We conducted a household study over the course of seven months, during which the commercially available Anki Vector robot was introduced to eight demographically diverse private homes for participants’ free use. Based on our findings, we conclude that the adoption process of personal companion robots slightly differs from that of functional vacuum cleaning robots, but may not differ substantially from technology adoption processes in general.

Human Social Feedback for Efficient Interactive Reinforcement Agent Learning

We are presenting a theory-driven approach to studying situated trust in HRI
by focusing on the experience of vulnerability. As a first proof-of-concept study, we conducted an interactive online survey that demonstrates that it is possible to measure human experience of vulnerability in the ordinary, mundane, and familiar situation of clothes shopping with a robot.

The robot may not notice my discomfort  Examining the Experience of Vulnerability for Trust in Human-Robot Interaction

The work presented evaluates distances in tactile Human-Drone Interaction.
A user study was conducted, in which a miniature-sized drone approached participants from four directions. With future scenarios at home or work in mind, the study was carried out in a seated position. The users were left the choice to engage in physical interaction by stopping the quadrotor by tapping to the quadrotor's frame or to stop it using a provided foot switch to provide more insights about their inhibition threshold.

B1: Evaluating Distances in Tactile Human-Drone Interaction

Formal verification receives increased importance since the complexity of safety-critical technical applications steadily increases. In our work, we describe an approach for symbolic model checking of collaborative robotic systems against safety properties. In particular, our methodology enables verification under system modifications and adaptive behavior generally represented as variability. Architectural, behavioral and environmental models are correspondingly abstracted to reach a balance of generality and verifiability with reasonable effort. By the use the model of a collaborative assembly use case we formally verify the existence of mechanical clamping hazards between the robot’s moving end effector and static environment objects.

Robot-Assisted Mindfulness Practice: Analysis of Neurophysiological Responses and Affective State Change

This work makes steps in the direction of integrating task constraints in visual action planning frameworks. The proposed approach handles robot-human interaction and employs LSTMs directly in the latent space representation achieving high accuracy.

Learning Task Constraints in Visual-Action Planning from Demonstrations

We describe INPRO, a framework that enables robots to learn board exercises by demonstration, with the aim of assisting caregivers during the monitoring of patients. INPRO encodes the learning task as a SAT planning problem, and comes up with a explainable list of STRIPS operators that summarizes the rules of the exercise. We validate our approach using two configurations of feedback in a user study with 37 participants.

Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games

We used an attention based deep Neural Network to predict the motion of a human during a handover task with a robot. We study the accuracy of the model during different phases of the handover operation.

A Humanoid Social Agent Embodying Physical Assistance Enhances Motor Training Experience

In this work we present a kinematics model for the humanoid robot Pepper. The model is based on analytical solutions of the inverse kinematics, which provide very accurate and fast results. It is envisioned to be used in a human-humanoid pose matching imitation system as it allows for translation of human joint positions to robot joint angles while maintaining the angle configuration of the body pose.

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Monocular 3D object detection for an indoor robot environment

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Multi-camera Torso Pose Estimation using Graph Neural Networks

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