Deep Reinforcement Learning of Abstract Reasoning from Demonstrations

Clark-Turner, Madison; Begum, Momotaz

doi:10.1145/3171221.3171289

Cited by 12 publications

(2 citation statements)

References 24 publications

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“…In this work, a Deep Q-Network (DQN) [18] was used to learn a mapping from visual input to one of several predefined actions for greeting people. Another work was conducted by Madson [23], where a DQN was used for learning generalized, high-level representations from both visual and auditory signals.…”

Section: A Deep Reinforcement Learning In Hrimentioning

confidence: 99%

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

Gao

Yang

Frisk

et al. 2019

2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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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. In this paper, we present a deep learning scheme that acquires a prior model of robot approaching behavior in simulation and applies it to real-world interaction with a physical robot approaching groups of humans. The scheme, which we refer to as Staged Social Behavior Learning (SSBL), considers different stages of learning in social scenarios. We learn robot approaching behaviors towards small groups in simulation and evaluate the performance of the model using objective and subjective measures in a perceptual study and a HRI user study with human participants. Results show that our model generates more socially appropriate behavior compared to a state-of-theart model.

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Section: A Deep Reinforcement Learning In Hrimentioning

confidence: 99%

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

Gao

Yang

Frisk

et al. 2019

2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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“…Despite the fact that there exists an extensive body of work on application-driven adaptation in HRI, the fast adaptation that is grounded in realistic perception remains a challenge [35]. Recent developments have explored different aspects of human-robot interaction including physical human-robot interaction [14], automatic reasoning [7] and affective human-robot interaction [13]. All these methods are successful in their own field, but view the social HRI process from a narrower perspective.…”

Section: Introductionmentioning

confidence: 99%

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

Gao

Sibirtseva

Castellano

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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

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A survey on deep reinforcement learning for audio-based applications

Latif

Cuayáhuitl

Pervez³

et al. 2022

Artif Intell Rev

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Deep reinforcement learning (DRL) is poised to revolutionise the field of artificial intelligence (AI) by endowing autonomous systems with high levels of understanding of the real world. Currently, deep learning (DL) is enabling DRL to effectively solve various intractable problems in various fields including computer vision, natural language processing, healthcare, robotics, to name a few. Most importantly, DRL algorithms are also being employed in audio signal processing to learn directly from speech, music and other sound signals in order to create audio-based autonomous systems that have many promising applications in the real world. In this article, we conduct a comprehensive survey on the progress of DRL in the audio domain by bringing together research studies across different but related areas in speech and music. We begin with an introduction to the general field of DL and reinforcement learning (RL), then progress to the main DRL methods and their applications in the audio domain. We conclude by presenting important challenges faced by audio-based DRL agents and by highlighting open areas for future research and investigation. The findings of this paper will guide researchers interested in DRL for the audio domain.

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Deep Reinforcement Learning of Abstract Reasoning from Demonstrations

Cited by 12 publications

References 24 publications

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

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

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

A survey on deep reinforcement learning for audio-based applications

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