Italy

Deploying robots with the ability to autonomously
navigate in the environment of a clinic necessitates carefully de-
signing their verbal and non-verbal behaviour in order to avoid
irritating, worrying or even endangering users, bystanders
and passersby. We describe a user-centred approach used to
design, implement and evaluate an interaction concept for
mobile robotic assistants for a clinic environment, resulting in
two robotic assistant prototypes and a generalized multimodal
interaction framework applicable to a variety of autonomously
navigating robotic agents. We present the steps of the design
process and intermediate findings, the final system components
and experiment results showing good acceptance of the inter-
action concept. A clip of the two robots in action can be found
under https://short.dfki.de/ro-man

IEEE RO-MAN 2022

Iterative User-Centric Development of Mobile Robotic Systems with Intuitive Multimodal Human-Robot Interaction in a Clinic Environment

**31st IEEE International Conference on Robot & Human Interactive Communication**
 This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.
 
 The conference covers a wide range of topics related to Robot and Human Interactive Communication, involving theories, methodologies, technologies, empirical and experimental studies. Papers related to the study of robotic technology, psychology, cognitive science, artificial intelligence, human factors, ethics and policies, interaction-based robot design and other topics related to human-robot interaction are welcome.
 
 **CONFERENCE THEME:**
 Social, asocial, and antisocial robots
 
 Robots are starting to populate our lives: at work, in our homes, hotels, shops. However, not everybody is comfortable with robots finding a place in society. For example, people may be afraid that robots will replace humans in their jobs. This belief is exacerbated by the media that, on one side, raise expectations of robots’ capabilities, while amplifying fears and worries on the other side.
 
 In the next years, due to the increasing request for interaction with people in social environments, academic research and market analyses predict that robots will become more and more “social”. But, at the same time, robots may incur the risk of being perceived as “asocial” as their pervasiveness in everyday environments may become an obstacle to social interaction between humans. Or they may even be rejected as “antisocial” if researchers do not take care to develop robots that behave according to the customs and habits of the communities where they are expected to operate.
 
 To prevent this from happening, robotic researchers can no longer take the luxury of being mere technicians. They must be capable of addressing all the challenges raised by future robot-populated communities in a multidisciplinary way. They need to share their knowledge with the public to make people aware of what robots really are, what expectations we can have from them, the risks we may incur, and the possible mitigation plans.
 
 While being open to any kind of contribution related to Human-Robot Interactive Communication, the RO-MAN 2022 conference will emphasize these aspects of robotic research. How can we create robots capable of being welcomed as our assistants and companions in society while, at the same time, not being perceived as “asocial” or “antisocial?” How can we create robots perceived as facilitators of the natural development of human beings and communities, rather than obstacles?
 
 Contributions from different disciplines, not exclusively related to technological development, will be welcomed as crucial to pave the way to global acceptance of future robots with an informed attitude.
 
 [![](https://assets.underline.io/uploads/markdown_image/1/image/af259abe105b3791c498ae1552d75710.png)](https://drive.google.com/file/d/18w73BZPB_JVOgcrcdZDqYpEupwVt4CnU/view?usp=sharing)

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31st IEEE International Conference on Robot & Human Interactive Communication
This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.

technical paper

We designed an observer-aware method for creating
navigation paths that simultaneously indicate a robot’s goal
while attempting to remain in view for a particular observer.
Prior art in legible motion does not account for the limited field
of view of observers, which can lead to wasted communication
efforts that are unobserved by the intended audience. Our
observer-aware legibility algorithm directly models the locations
and perspectives of observers, and places legible movements
where they can be easily seen. To explore the effectiveness of
this technique, we performed a 300-person online user study.
Users viewed first-person videos of restaurant scenes with robot
waiters moving along paths optimized for different observer
perspectives, along with a baseline path that did not take into
account any observer’s field of view. Participants were asked to
report their estimate of how likely it was the robot was heading
to their table versus the other goal table as it moved along each
path. We found that for observers with incomplete views of the
restaurant, observer-aware legibility is effective at increasing
the period of time for which observers correctly infer the goal
of the robot. Non-targeted observers have lower performance
on paths created for other observers than themselves, which
is the natural drawback of personalizing legible motion to a
particular observer. We also find that an observer’s relationship
to the environment (e.g. what is in their field of view) has
more influence on their inferences than the observer’s relative
position to the targeted observer, and discuss how this implies
knowledge of the environment is required in order to effectively
plan for multiple observers at once.

Observer-Aware Legibility for Social Navigation

As a collaboration between humans and robots becomes critical, stiffness control of the robot is essential for stability and efficiency of work. Therefore, research on the variable stiffness mechanism is being actively conducted in service robots, soft robots, and exoskeletons. The main types of variable stiffness mechanisms are jamming effect (particle jamming and layer jamming), shape memory polymer (SMP), and low melting point alloy (LMPA). The case of the jamming effect uses negative pneumatic pressure to change the stiffness. Because of that, it is possible to change the stiffness quickly, and it is easy to manufacture. However, both SMP and LMPA use thermal energy to increase the material's stiffness. There is a risk of damage to humans or robots, and it takes much time to change the stiffness. Therefore, this study introduces a variable stiffness mechanism that combines particle jamming and core-frame. In addition, optimization studies are being conducted to use the jamming effect in industries. However, due to the randomness of particle jamming, the existing studies assumed that the variable stiffness mechanism was a simple beam or modeled it using hook's law, so the accuracy was low. Therefore, in this study, five design variables are selected for particle and core-frame, the main elements constituting the variable stiffness mechanism. In addition, design variables are optimized through various FEM simulations. Furthermore, the simulation is proved by establishing a theoretical model for variable stiffness structure when the jamming effect occurs. Finally, the optimization of five design variables is proved through experiments.

Exploring Older Adults' Acceptance, Needs and Design Requirements towards Applying Social Robots in a Rehabilitation Context

The advent of robots in our daily life depends on
their ability to navigate and intervene efficiently in our
environment. Whether considering household or industrial
applications, one of the most important functions they should
have is the ability to grasp and manipulate a large amount of
objects, tools and machines that can vary in form, size and
weight, but share the fact that they were designed for humans
and have functional elements, e.g. buttons or handles, fitted to
the human hand. The development of biologically inspired
anthropomorphic robotic hands thus appears as a natural
research path to allow robots replicating these activities. Such
devices however prove to be complex to design and control, and
they remain in practice limited to date to laboratory
experiments. They hardly reach a sufficient simplicity,
robustness and cost allowing for their widespread adoption in
our houses or factories and industrial robots still make use of
simple bi-digital grippers or dedicated tools which in turn
suffer a poor versatility. To overcome this situation, novel
dexterous grippers are required, that are sufficiently versatile
to adapt to various situations and objects yet simple enough
and cost effective. This compromise is however difficult to
achieve and the specification of such grippers is still an open
issue. This paper introduces a human-centered manual
interaction patterns analysis methodology that intends to
contribute to fill this gap. After a presentation of our approach,
we apply it in different contexts and show how it can be used to
orient a robotic gripper design that will fit given use-case
requirements.

Augmented Environment Representations with Complete Object Models

Risk is traditionally described as the expected likelihood of an undesirable outcome, such as a collision for an autonomous vehicle. Accurately predicting risk or potentially risky situations is critical for the safe operation of an autonomous vehicle. 
This work combines use of a controller trained to navigate around individuals in a crowd and a risk-based decision-making framework for an autonomous vehicle that integrates high-level risk-based path planning with a reinforcement learning-based low-level control. We evaluated our method using a high-fidelity simulation environment. We show our method results in zero collisions with pedestrians and predicted the least risky path, time to travel, or day to travel in approximately 72\% of traversals. This work can improve safety by allowing an autonomous vehicle to one day avoid and react to risky situations.

Comparison of Vehicle-To-Bicyclist and Vehicle-To-Pedestrian Communication Feedback Module: A Study on Increasing Legibility, Public Acceptance and Trust

The use of hand-based gestures has been proposed as an intuitive way for people to communicate with robots. Typically the set of gestures is defined by the experimenter. However, existing works do not necessarily focus on gestures that are communicative, and it is unclear whether the selected gesture are actually intuitive to users. This paper investigates whether different people inherently use similar gestures to convey the same commands to robots, and how teaching of gestures when collecting demonstrations for training recognizers can improve resulting accuracy. We conducted this work in two stages. In Stage 1, we conducted an online user study (n=190) to investigate if people use similar gestures to communicate the same set of given commands to a robot when no guidance or training was given. Results revealed large variations in the gestures used among individuals With the absences of training. Training a gesture recognizer using this dataset resulted in an accuracy of around 20%. In response to this, Stage 2 involved proposing a common set of gestures for the commands. We taught these gestures through demonstrations and collected ~7500 videos of gestures from study participants to train another gesture recognition model. Initial results showed improved accuracy but a number of gestures had high confusion rates. Refining our gesture set and recognition model by removing those gestures, We achieved an final accuracy of 84.1±2.4%. We integrated the gesture recognition model into the ROS framework and demonstrated a use case, where a person commands a robot to perform a pick and place task using the gesture set.

A Microsociological Approach to Understanding the Boundary Between Robot Cooperativeness and Uncooperativeness in Human-Robot Collaboration

Agriculture is facing a labor crisis, leading to increased interest in fleets of small, under-canopy robots (agbots) that can perform precise, targeted actions (e.g., crop scouting, weeding, fertilization), while being supervised by human operators remotely. However, farmers are not necessarily experts in robotics technology and will not adopt technologies that add to their workload or do not provide an immediate payoff. In this work, we explore methods for communication between a remote human operator and multiple agbots and examine the impact of audio communication on the operator's preferences and productivity. We develop a simulation platform where agbots are deployed across a field, randomly encounter failures, and call for help from the operator. As the agbots report errors, various audio communication mechanisms are tested to convey which robot failed and what type of failure occurs. The human is tasked with verbally diagnosing the failure while completing a secondary task. A user study was conducted to test three audio communication methods: earcons, single-phrase commands, and full sentence communication. Each participant completed a survey to determine their preferences and each method's overall effectiveness. Our results suggest that the system using single phrases is the most positively perceived by participants and may allow for the human to complete the secondary task more efficiently. The code is available at: https://github.com/akamboj2/Agbot-Sim.

Spatio-Temporal Action Order Representationfor Mobile Manipulation Planning

Today, people are enjoying using teleconference systems
like Zoom or Skype for social communication or for having
drinks through a screen with others via the internet. In
addition, some people have started using embodied systems
called telepresence robots, such as the Beam robot. Some
schools have started using telepresence robots so that
students can attend school. However, in previous studies,
systems have not been compared in terms of social presence
and anthropomorphism, for example, robots compared with
humans. Therefore, we wondered how the presence and
anthropomorphism of such systems affect people. Therefore,
we carried out a web-based experiment and conducted a
one-way ANOVA (smartphone vs. telepresence robot with vs.
without motion). Some people feel that telepresence robots
bring a feeling of presence to remote places. Ironically,
from the results, a video teleconference system using a
smartphone and a telepresence robot did not create a
feeling of presence, but regarding anthropomorphism,
participants felt more of a human-likeness in the video
teleconference system.

Physical embodiment vs. smartphone: which influences presence and anthropomorphism most in telecommunication?

Theory of Mind (ToM) is a psychological construct that captures the ability to ascribe mental states to others and then use those representations for explaining and predicting behavior. We review recent progress in endowing artificially intelligent robots with ToM. A broad array of modeling, experimental, and benchmarking approaches and methods are present in the extant literature. Unlike other domains of human cognition for which research has achieved super-human capabilities, ToM for robots lacks a unified construct and is not consistently benchmarked or validated---realities which possibly hinder progress in this domain. We argue that this is, at least in part, due to inconsistent defining of ToM, no presence of a unifying modeling construct, and the absence of a shared data resource. We believe these would improve the ability of the research community to compare the ToM abilities of different systems. We suggest that establishing a shared definition of ToM, creating a shared data resource that supports consistent benchmarking & validation, and developing a generalized modeling tool are critical steps towards giving robots ToM capabilities that lay observers will recognize as such.

Robots with Theory of Mind for Humans: A Survey

This paper develops a novel framework called Providers-Clients-Robots (PCR), applicable to socially assistive robots that support research on shared understanding in human-robot interactions. Providers, Clients, and Robots share an actionable and intuitive representation of the environment to create plans that best satisfy the combined needs of all parties. The plans are formed via interaction between the Client and the Robot based on a previously built multi-modal navigation graph. The explainable environmental representation in the form of a navigation graph is constructed collaboratively between Providers and Robots prior to interaction with Clients. We develop a realization of the proposed framework to create a spatial semantic representation of an indoor environment autonomously. Moreover, we develop a planner that takes in constraints from Providers and Clients of the establishment and dynamically plans a sequence of visits to each area of interest. Evaluations show that the proposed realization of the PCR framework can successfully make plans while satisfying the specified time budget and sequence constraints and outperforming the greedy baseline.

Providers-Clients-Robots: Framework for spatial-semantic planning for shared understanding in human-robot interaction

Recent advances in collaborative robots have provided an
opportunity for the close collaboration of humans and
robots in a shared workspace. To exploit this
collaboration, robots need to plan for optimal team
performance while considering human presence and
preference. This paper studies the problem of task
selection and planning in a collaborative, simulated
scenario. In contrast to existing approaches, which mainly
involve assigning tasks to agents by a task allocation unit
and informing them through a communication interface, we
give the human and robot the agency to be the leader or
follower. This allows them to select their own tasks or
even assign tasks to each other. We propose a task
selection and planning algorithm that enables the robot to
consider the human's preference to lead, as well as the
team and the human's performance, and adapts itself
accordingly by taking or giving the lead. The effectiveness
of this algorithm has been validated through a simulation
study with different combinations of human accuracy levels
and preferences for leading.

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Iterative User-Centric Development of Mobile Robotic Systems with Intuitive Multimodal Human-Robot Interaction in a Clinic Environment

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Observer-Aware Legibility for Social Navigation

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