Learning Bodily Expression of Emotion for Social Robots Through Human Interaction

Tuyen, Nguyen Tan Viet; Elibol, Armağan; Chong, Nak Young

doi:10.1109/tcds.2020.3005907

Cited by 19 publications

(3 citation statements)

References 48 publications

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“…The actual developmental level characterizes mental development retrospectively, while the Zone of Proximal Development characterizes mental development prospectively. For example, we may quote a representative list of studies in the two preceding decades that cover in isolation some of the specific issues of developmental robotics: Self-exploration and early imitation (Kuniyoshi et al, 2003), Modeling joint attention (Nagai, 2007), Scaffolding Robot Action Learning (Nagai and Rohlfing, 2009), Affordance-based perception (Min et al, 2016), Bootstrapping the semantics of tools (Schoeler and Wörgötter, 2016), Perception of Localized Features (Giagkos et al, 2017), Bootstrapping of Sensory-Motor Skills (Wieser and Cheng, 2018), Developing Reaching Ability like human infants (Luo et al, 2018), Sensorimotor Communication (Donnarumma et al, 2012(Donnarumma et al, , 2018Pezzulo et al, 2019), Integration of Sensing, Cognition, Learning, and Control (Li et al, 2019), Evaluation of Internal Models (Smith and Herrmann, 2019), Emergence of symbolic representations (Ugur et al, 2015;Taniguchi et al, 2019), Grounded affordances (Saponaro et al, 2020), Bodily Expression of Emotion (Tuyen et al, 2021), Morphological development (Naya-Varela et al, 2021), Skill Learning Strategy with Dynamic Motion Primitives (Li et al, 2021), Interest-driven exploration (Rayyes et al, 2022(Rayyes et al, , 2023.…”

Section: Principles Of Developmental Roboticsmentioning

confidence: 99%

Artificial cognition vs. artificial intelligence for next-generation autonomous robotic agents

Sandini,

Sciutti,

Morasso

2024

Front. Comput. Neurosci.

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The trend in industrial/service robotics is to develop robots that can cooperate with people, interacting with them in an autonomous, safe and purposive way. These are the fundamental elements characterizing the fourth and the fifth industrial revolutions (4IR, 5IR): the crucial innovation is the adoption of intelligent technologies that can allow the development of cyber-physical systems, similar if not superior to humans. The common wisdom is that intelligence might be provided by AI (Artificial Intelligence), a claim that is supported more by media coverage and commercial interests than by solid scientific evidence. AI is currently conceived in a quite broad sense, encompassing LLMs and a lot of other things, without any unifying principle, but self-motivating for the success in various areas. The current view of AI robotics mostly follows a purely disembodied approach that is consistent with the old-fashioned, Cartesian mind-body dualism, reflected in the software-hardware distinction inherent to the von Neumann computing architecture. The working hypothesis of this position paper is that the road to the next generation of autonomous robotic agents with cognitive capabilities requires a fully brain-inspired, embodied cognitive approach that avoids the trap of mind-body dualism and aims at the full integration of Bodyware and Cogniware. We name this approach Artificial Cognition (ACo) and ground it in Cognitive Neuroscience. It is specifically focused on proactive knowledge acquisition based on bidirectional human-robot interaction: the practical advantage is to enhance generalization and explainability. Moreover, we believe that a brain-inspired network of interactions is necessary for allowing humans to cooperate with artificial cognitive agents, building a growing level of personal trust and reciprocal accountability: this is clearly missing, although actively sought, in current AI. The ACo approach is a work in progress that can take advantage of a number of research threads, some of them antecedent the early attempts to define AI concepts and methods. In the rest of the paper we will consider some of the building blocks that need to be re-visited in a unitary framework: the principles of developmental robotics, the methods of action representation with prospection capabilities, and the crucial role of social interaction.

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Section: Principles Of Developmental Roboticsmentioning

confidence: 99%

Artificial cognition vs. artificial intelligence for next-generation autonomous robotic agents

Sandini,

Sciutti,

Morasso

2024

Front. Comput. Neurosci.

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“…Their results showed that the pattern of reactions to humans was more VOLUME , 2022 favorable for anthropomorphic robots than for mechanistic robots. Tuyen et al [45] designed emotional bodily expressions for a robot that adopts the user's emotional gestures. They proposed an action selection and transformation model that enables the robot to progressively learn from the user's gestures, identify the user's habitual behaviors, and transform the selected behaviors into robot actions.…”

Section: Empathy In Human-robot Interactionmentioning

confidence: 99%

Influence of Anthropomorphic Agent on Human Empathy Through Games

Tsumura

Yamada

2023

IEEE Access

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The social acceptance of AI agents, including intelligent virtual agents and physical robots, is becoming more important for the integration of AI into human society. Although the agents used in human society share various tasks with humans, their cooperation may frequently reduce the task performance. One way to improve the relationship between humans and AI agents is to have humans empathize with the agents. By empathizing, humans feel positively and kindly toward agents, which makes it easier to accept them. In this study, we focus on tasks in which humans and agents have various interactions together, and we investigate the properties of agents that significantly influence human empathy toward the agents. To investigate the effects of task content, difficulty, task completion, and an agent's expression on human empathy, two experiments were conducted. The results of the two experiments showed that human empathy toward the agent was difficult to maintain with only task factors, and that the agent's expression was able to maintain human empathy. In addition, a higher task difficulty reduced the decrease in human empathy, regardless of task content. These results demonstrate that an AI agent's properties play an important role in helping humans accept them.

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“…In recent years, increasing attention is attracted to 3D human pose estimation in videos, due to its wide application in the field of action recognition [1], robot learning with bodily expression [2], and human-computer interaction [3]. The stateof-the-art approaches [4]- [6] are mostly in two steps, 2D keypoint detection, and 3D pose estimation from the 2D keypoints.…”

Section: Introductionmentioning

confidence: 99%

Motion Projection Consistency Based 3D Human Pose Estimation with Virtual Bones from Monocular Videos

Wang¹,

Honghao²,

Wang³

et al. 2021

Preprint

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Real-time 3D human pose estimation is crucial for human-computer interaction. It is cheap and practical to estimate 3D human pose only from monocular video. However, recent bone splicing based 3D human pose estimation method brings about the problem of cumulative error. In this paper, the concept of virtual bones is proposed to solve such a challenge. The virtual bones are imaginary bones between non-adjacent joints. They do not exist in reality, but they bring new loop constraints for the estimation of 3D human joints. The proposed network in this paper predicts real bones and virtual bones, simultaneously. The final length of real bones is constrained and learned by the loop constructed by the predicted real bones and virtual bones. Besides, the motion constraints of joints in consecutive frames are considered. The consistency between the 2D projected position displacement predicted by the network and the captured real 2D displacement by the camera is proposed as a new projection consistency loss for the learning of 3D human pose. The experiments on the Human3.6M dataset demonstrate the good performance of the proposed method. Ablation studies demonstrate the effectiveness of the proposed inter-frame projection consistency constraints and intra-frame loop constraints.

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Learning Bodily Expression of Emotion for Social Robots Through Human Interaction

Cited by 19 publications

References 48 publications

Artificial cognition vs. artificial intelligence for next-generation autonomous robotic agents

Artificial cognition vs. artificial intelligence for next-generation autonomous robotic agents

Influence of Anthropomorphic Agent on Human Empathy Through Games

Motion Projection Consistency Based 3D Human Pose Estimation with Virtual Bones from Monocular Videos

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