Towards Autonomous Collaborative Robots that Adapt and Explain

Peter Tisnikar; Lennart Wachowiak; Gerard Canal; Andrew Coles; Matteo Leonetti; Oya Celiktutan

Towards Autonomous Collaborative Robots that Adapt and Explain

Peter Tisnikar^*, Lennart Wachowiak^*, Gerard Canal, Andrew Coles, Matteo Leonetti, Oya Celiktutan

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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Abstract

As we will increasingly encounter robots in our everyday lives, engage with them in social interactions, and collaborate on common tasks, it is important we endow them with the capability of adapting to our abilities and preferences. Moreover, we want them to be able to explain the decisions they make in collaborative tasks to maximise rapport and build trust and acceptance. Current explanations are missing a focus on the individual user’s needs, which is why we want to learn when and what to explain in a collaboration. This paper proposes the roadmap that we plan to implement with the goal of inferring the user’s mental state from physiological and social signals in order to inform explanation generation and robot adaptation. We present preliminary results utilizing eye gaze and a planned framework that allows a collaborative robot to adapt to its human collaborator and tailor its explanations to them in order to minimise the confusion in an interaction.

Original language	English
Title of host publication	IEEE ICRA 2022 Workshop on Prediction and Anticipation Reasoning in Human Robot Interaction
Publication status	Published - 9 Jun 2022

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abstract = "As we will increasingly encounter robots in our everyday lives, engage with them in social interactions, and collaborate on common tasks, it is important we endow them with the capability of adapting to our abilities and preferences. Moreover, we want them to be able to explain the decisions they make in collaborative tasks to maximise rapport and build trust and acceptance. Current explanations are missing a focus on the individual user{\textquoteright}s needs, which is why we want to learn when and what to explain in a collaboration. This paper proposes the roadmap that we plan to implement with the goal of inferring the user{\textquoteright}s mental state from physiological and social signals in order to inform explanation generation and robot adaptation. We present preliminary results utilizing eye gaze and a planned framework that allows a collaborative robot to adapt to its human collaborator and tailor its explanations to them in order to minimise the confusion in an interaction.",

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T1 - Towards Autonomous Collaborative Robots that Adapt and Explain

AU - Tisnikar, Peter

AU - Wachowiak, Lennart

AU - Canal, Gerard

AU - Coles, Andrew

AU - Leonetti, Matteo

AU - Celiktutan, Oya

PY - 2022/6/9

Y1 - 2022/6/9

N2 - As we will increasingly encounter robots in our everyday lives, engage with them in social interactions, and collaborate on common tasks, it is important we endow them with the capability of adapting to our abilities and preferences. Moreover, we want them to be able to explain the decisions they make in collaborative tasks to maximise rapport and build trust and acceptance. Current explanations are missing a focus on the individual user’s needs, which is why we want to learn when and what to explain in a collaboration. This paper proposes the roadmap that we plan to implement with the goal of inferring the user’s mental state from physiological and social signals in order to inform explanation generation and robot adaptation. We present preliminary results utilizing eye gaze and a planned framework that allows a collaborative robot to adapt to its human collaborator and tailor its explanations to them in order to minimise the confusion in an interaction.

AB - As we will increasingly encounter robots in our everyday lives, engage with them in social interactions, and collaborate on common tasks, it is important we endow them with the capability of adapting to our abilities and preferences. Moreover, we want them to be able to explain the decisions they make in collaborative tasks to maximise rapport and build trust and acceptance. Current explanations are missing a focus on the individual user’s needs, which is why we want to learn when and what to explain in a collaboration. This paper proposes the roadmap that we plan to implement with the goal of inferring the user’s mental state from physiological and social signals in order to inform explanation generation and robot adaptation. We present preliminary results utilizing eye gaze and a planned framework that allows a collaborative robot to adapt to its human collaborator and tailor its explanations to them in order to minimise the confusion in an interaction.

M3 - Conference paper

BT - IEEE ICRA 2022 Workshop on Prediction and Anticipation Reasoning in Human Robot Interaction

ER -

Towards Autonomous Collaborative Robots that Adapt and Explain

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