Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human-Robot Interaction
In socially assistive robotics, an important research area is the development of adaptation techniques and their effect on human-robot interaction. We present a meta-learning based policy gradient method for addressing the problem of adaptation in human-robot interaction and also investigate its role as a mechanism for trust modelling. By building an escape room scenario in mixed reality with a robot, we test our hypothesis that bi-directional trust can be influenced by different adaptation algorithms. We found that our proposed model increased the perceived trustworthiness of the robot and influenced the dynamics of gaining human's trust. Additionally, participants evaluated that the robot perceived them as more trustworthy during the interactions with the meta-learning based adaptation compared to the previously studied statistical adaptation model.
Code (0)
등록된 구현이 없습니다.
Tasks
Meta-LearningMeta Reinforcement LearningMixed Realityreinforcement-learningReinforcement LearningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
Sparse Meta Networks for Sequential Adaptation and its Application to Adaptive Language Modelling
Training a deep neural network requires a large amount of single-task data and involves a long time-consuming optimization phase. This is not scalable to complex, realistic environments with new unexpected changes. Human…
Incremental LearningInductive BiasLanguage ModellingMeta-Learning+1Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation
Learning new task-specific skills from a few trials is a fundamental challenge for artificial intelligence. Meta reinforcement learning (meta-RL) tackles this problem by learning transferable policies that support few-sh…
Meta Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Hypothesis Network Planned Exploration for Rapid Meta-Reinforcement Learning Adaptation
Meta Reinforcement Learning (Meta RL) trains agents that adapt to fast-changing environments and tasks. Current strategies often lose adaption efficiency due to the passive nature of model exploration, causing delayed un…
Meta Reinforcement Learningreinforcement-learningReinforcement LearningBayesian Meta-reinforcement Learning for Traffic Signal Control
In recent years, there has been increasing amount of interest around meta reinforcement learning methods for traffic signal control, which have achieved better performance compared with traditional control methods. Howev…
Continual LearningMeta-LearningMeta Reinforcement Learningreinforcement-learning+3Linear Representation Meta-Reinforcement Learning for Instant Adaptation
This paper introduces Fast Linearized Adaptive Policy (FLAP), a new meta-reinforcement learning (meta-RL) method that is able to extrapolate well to out-of-distribution tasks without the need to reuse data from training,…
continuous-controlContinuous ControlMeta Reinforcement Learningreinforcement-learning+2