Search Results for author: Ginevra Castellano

Found 6 papers, 1 papers with code

I Can See it in Your Eyes: Gaze as an Implicit Cue of Uncanniness and Task Performance in Repeated Interactions

no code implementations13 Jan 2021 Giulia Perugia, Maike Paetzel-Prüsmann, Madelene Alanenpää, Ginevra Castellano

These are key findings for the HRI community as they entail that gaze behavior can be used as an implicit measure of people's perception of robots in a social chat and of their engagement and task performance in a joint task.

Robotics Human-Computer Interaction

Explainable Agents Through Social Cues: A Review

no code implementations11 Mar 2020 Sebastian Wallkotter, Silvia Tulli, Ginevra Castellano, Ana Paiva, Mohamed Chetouani

One reason for this high variance in terminology is the unique array of social cues that embodied agents can access in contrast to that accessed by non-embodied agents.

Skew-Explore: Learn faster in continuous spaces with sparse rewards

no code implementations25 Sep 2019 Xi Chen, Yuan Gao, Ali Ghadirzadeh, Marten Bjorkman, Ginevra Castellano, Patric Jensfelt

In this work, we introduce an exploration approach based on maximizing the entropy of the visited states while learning a goal-conditioned policy.

Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human-Robot Interaction

no code implementations12 Aug 2019 Yuan Gao, Elena Sibirtseva, Ginevra Castellano, Danica Kragic

In socially assistive robotics, an important research area is the development of adaptation techniques and their effect on human-robot interaction.

Meta-Learning Meta Reinforcement Learning +3

Learning Socially Appropriate Robot Approaching Behavior Toward Groups using Deep Reinforcement Learning

1 code implementation16 Oct 2018 Yuan Gao, Fangkai Yang, Martin Frisk, Daniel Hernandez, Christopher Peters, Ginevra Castellano

Deep reinforcement learning has recently been widely applied in robotics to study tasks such as locomotion and grasping, but its application to social human-robot interaction (HRI) remains a challenge.

reinforcement-learning Reinforcement Learning (RL)

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