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Based on a user-study with experts in motion planning algorithms, we propose a taxonomy of explanations, including kinds of questions/explanations/methods related to motion planning, and a set of design considerations for explainable motion planning.

IEEE RO-MAN 2021

How experts explain motion planner output: a preliminary user-study to inform the design of explainable planners

xaip

explainable planning

motion planning

explainability

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

Subsequent AAMAS conferences have been held in Melbourne, Australia (July 2003), New York City, NY, USA (July 2004), Utrecht, The Netherlands (July 2005), Hakodate, Japan (May 2006), Honolulu, Hawaii, USA (May 2007), Estoril, Portugal (May 2008), Budapest, Hungary (May 2009), Toronto, Canada (May 2010), Taipei, Taiwan (May 2011), Valencia, Spain (June 2012), Minnesota, USA (May 2013), Paris, France (May 2014), Istanbul, Turkey (May 2015), Singapore (May 2016), São Paulo (2017) and Stockholm, Sweden (2018), Montreal (May 2019), Auckland (May 2020, Virtual), London (May 2021, Virtual).
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A ticket is required to attend this event, please register using the link below:

https://aamas2022-conference.auckland.ac.nz/attending/registration/

Registration Is Required

AAMAS 2022

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

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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

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

A novel Synthesized Trust Learning (STL) method was developed to model human trust in the collaboration. STL explored using heterogeneous human feedback to model human trust. Specifically, human operator’s trust level and trust preference.

A Laser Projection System for

To build an agent providing assistance to human rescuers in an urban search and rescue task, it is crucial to predict their strategies. Developing data-driven models to predict them requires costly data collection and training for each new environment. We investigate transfer learning to mitigate this challenge (a) from a source environment with smaller number of types of injured victims to one with more types and (b) from a simpler environment to a more complex one for navigation strategy.

A Layered Control Approach to Human-Aware Task and Motion

Illusory Control that we proposed is a new teleoperation method that aims to increase the sensory value of comfort and happiness in teleoperation of mobile robots by providing visual feedback that seems to trick the operator. By conducting subject experiments, we compared the proposed method with conventional teleoperation methods such as direct teleoperation and shared control, and showed the possibility of improving task efficiency and system acceptance.

Teleoperation Method by Illusion of Human Intention and Time

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

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.

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How experts explain motion planner output: a preliminary user-study to inform the design of explainable planners

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