United States

Interactive Task Learning is a research approach that studies the design of intelligent agents that can acquire knowledge from human instruction. This research has largely been robot-centered. We study this problem from a human-centered perspective. We conduct an interactive teaching study (N=10), and conduct a cognitive task analysis to characterize various aspects of teaching actions executed in natural human teaching. We propose ITL robot design recommendations using a computational theory SharedPlans that can support different participant teaching styles.

IEEE RO-MAN 2021

Unpacking Human Teachers Intentions for Natural Interactive Task Learning

communicative actions

sharedplans

interactive task learning

**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)

A registration is required to access this event page. If you have not already done so, please use the below link to register for the event. 

[Register Here](http://www.smile.unina.it/ro-man2022/welcome-to-ieee-ro-man-2022/)

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IEEE RO-MAN 2022

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

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. 

To explore the interaction design of an autonomous social robot stationed in a hotel room, we conducted a Wizard of Oz study. The results also suggest novel forms of the hotel room robot’s interactive services, such as a hotel-life management service and conversation partner service as a role of a listening presence, which draws out and understands with the guests’ talk.

When Would You Trust a Robot? A Study onTrust and Theory of Mind in Human-Robot Interactions

We have conducted a crowdsourced study to explore the feasibility of using robots in the context of bullying (i.e., to investigate whether robots are perceived as entities that can be bullied). 
We present qualitative and quantitative results from a between-subjects video study, comparing robot bullying (robots being bullied) to human bullying (humans being bullied). 
Our findings suggest that while the majority of participants describe both instances with connotations of wrongness and immorality, they use different cognitive mechanisms for moral disengagement with robot bullying vs human bullying. We also found significant differences in participants' perceptions of each scenario, including associating robot mistreatment with bullying less strongly, and being less willing to intervene in it.

Teaching Robots Novel Objects by Pointing at Them

This work is concerned with the center of mass estimator for bipedal robots and aims at developing its new design framework with linear inverted pendulum model of bipedal robots. This work constructs an assumption that the variations of the CoM height from a fixed point can be regarded as bounded persistent unknown elements. Then we develop the l1 optimal CoM estimator, by which the effect of the bounded persistent elements on the CoM estimation would be minimized.

On the L1 Optimal State Estimator with Application to Bipedal Robots

While everyday-inspired tasks are interesting as reflecting a realistic use of robots, they often contain a lot of unknown and uncontrolled conditions and specific robot behavior can be hard to test. In this presentation, we propose a new psychology-inspired task, gathering perspective-taking, planning, knowledge representation with theory of mind, manipulation, and communication. Along with a description of the task, we present a cognitive robot architecture able to perform it in its nominal cases.

Understanding Human-Robot Collaboration for People with Mobility Impairments at the Workplace

We present a visual perception system for social robots
that is able to detect a person’s engagement in an interaction
from nonverbal cues independently of principal user activity.
This was achieved by the introduction of a set of proxemics,
body posture and attention features extracted with a Kinect sensor.
The system simultaneously classifies if a person is attempting to interact with the camera and regresses the user's level of availability. Finally, a prototype was implemented which is able to run both previous estimates in real-time using a shared feature vector.

PredGaze:A Incongruity Prediction Model for User’s Gaze Movement

We propose a framework for solving the mult-robot informating gathering problem leveraging a Gaussian Mixture Model (GMM) as prior knowledge for modeling informative regions in the target area. Taking samples from the GMM, informative robot paths are then computed that optimize the travel costs subject to energy budgets. Decomposing these paths into waypoints, robot trajectories are planned while respecting kinematic constraints and subsequently replanned online to
avoid collisions among themselves. The GMM model is incrementally updated by incorporating the gathered information. We demonstrate that our framework can achieve a significant amount of information gain with the optimal travel distance.

Education for New Human–Robot Coexistence: Countereffect and “Paragmatism” for the Weak Robot

In this talk, we address the problem of human-robot coordination in sequences of manipulation tasks. Our approach integrates human motion prediction with Task and Motion planning (TAMP) to address human-robot collaboration tasks. We first devise a hierarchical motion prediction approach by combining Inverse Reinforcement Learning and short-term motion prediction using a Recurrent Neural Network. In a second step, we extend a dynamic version of the TAMP algorithm Logic-Geometric Programming (LGP), which replans periodically to handle the mismatch between the human motion prediction and the actual human behavior.

Hierarchical Human-Motion Prediction and Logic-Geometric Programming for Minimal Interference Human-Robot Tasks

This paper proposes a human-aware motion planner building on RRT-Connect, dubbed Human-Aware RRT-Connect. The planner considers a composite cost function that includes four criteria: human separation distance, human-robot center of mass distance, robot inertia, and visibility. A simulation study is conducted where Human-Aware RRT-Connect is compared to other offline planners through a set of problem scenarios. The paths generated by the Human-Aware RRT-Connect planner maintain larger separation distances from the human, are of lower inertia, and are more visible.

Human-Aware RRT-Connect: Motion Planning for Safe Human-Robot Collaboration

We developed a high-level decision-making subsystem, a model-based context classifier, and a multi-objective optimization-based local planner to achieve socially-aware trajectories for autonomously sensed contexts. Using a context classification system, the robot can select social objectives that are later used by Pareto Concavity Elimination Transformation (PaCcET) based local planner to generate safe, comfortable, and socially appropriate trajectories for its environment. This was tested and validated in multiple environments on a Pioneer mobile robot platform.

DroneLight: Drone Draws in the Air using Long Exposure Light Painting and ML

The main contribution of this work is in the development of a long-term 12-month older adult co-design methodology for home social robot design with long-term co-design guidelines for how other researchers can adopt the methodology. Through the process, we promote co-design as a human-centered approach for designing robots, empowering older adults in the design of these technologies.

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Unpacking Human Teachers Intentions for Natural Interactive Task Learning

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When Would You Trust a Robot? A Study onTrust and Theory of Mind in Human-Robot Interactions

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Trustworthiness assessment in multimodal human-robot interaction based on cognitive load

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Premium content

Unpacking Human Teachers Intentions for Natural Interactive Task Learning

Next from IEEE RO-MAN 2021

When Would You Trust a Robot? A Study onTrust and Theory of Mind in Human-Robot Interactions

Similar lecture

Trustworthiness assessment in multimodal human-robot interaction based on cognitive load

Stay up to date with the latest Underline news!

PRESENTATIONS

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COMPANY

RESOURCES

Unpacking Human Teachers Intentions for Natural Interactive Task Learning