United States

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.

IEEE RO-MAN 2021

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

socially-aware navigation

human-robot interaction

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020

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. 

In this paper, a teleoperated robot prototype was designed and tested to allow law enforcement officers to remotely and transparently communicate and interact with others. The robot featured near face-to-face interactivity and accuracy across multiple verbal and non-verbal modes using screens, microphones, and speakers. In cooperation with multiple law enforcement agencies, results are presented on this dynamic and integrative teleoperated communicative robot platform in terms of attitudes towards robots, trust in robot operation, and trust in human-robot-human interaction and communication.

Analyzing Human-Robot Trust in Police Work Using a Teleoperated Communicative Robot

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

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.

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

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Analyzing Human-Robot Trust in Police Work Using a Teleoperated Communicative Robot

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