A MultiModal Social Robot Toward Personalized Emotion Interaction
Not on the map. Why?
Abstract (arXiv)
Human emotions are expressed through multiple modalities, including verbal and non-verbal information. Moreover, the affective states of human users can be the indicator for the level of engagement and successful interaction, suitable for the robot to use as a rewarding factor to optimize robotic behaviors through interaction. This study demonstrates a multimodal human-robot interaction (HRI) framework with reinforcement learning to enhance the robotic interaction policy and personalize emotional interaction for a human user. The goal is to apply this framework in social scenarios that can let the robots generate a more natural and engaging HRI framework.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 30 references that are in the map. First posted on arXiv 8 Oct 2021.
Builds on
8 of 30 references.
References · 8
- 2017Attention Is All You Needcited by 60,361
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2019Fine-Tuning Language Models from Human Preferencescited by 2,150
- 2019Multimodal Transformer for Unaligned Multimodal Language Sequencescited by 645
- 2018MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversationscited by 643
- 2020The EMPATHIC Framework for Task Learning from Implicit Human Feedbackcited by 38
- 2018Intrinsically motivated reinforcement learning for human-robot interaction in the real-worldcited by 12
- 2017Multi-modal Conditional Attention Fusion for Dimensional Emotion Predictioncited by 7
Built on by
Citing papers per year: 2022: 1.
1 paper.
- 2022Observed Adversaries in Deep Reinforcement Learningcited by 0
- 2022Observed Adversaries in Deep Reinforcement Learning13 Oct 2022