Hierarchical emotional episodic memory for social human robot collaboration

Lee, WonHyong; Kim, Jong-Hwan

doi:10.1007/s10514-017-9679-0

Cited by 16 publications

(6 citation statements)

References 27 publications

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“…Intuitive strain capacity: When handling cases, human lawyers will combine rational logical analysis and perceptual intuitive reactions to make judgments. By contrast, a robot's understanding of grammar and wording, along with its emotional intuitive response, is not as responsive as that of humans (Lee & Kim, 2018). In particular, the intuitive response to human emotions is too complex for robots (Menne & Schwab, 2018).…”

Section: Irreplaceable Human Abilitiesmentioning

confidence: 97%

Adopting robot lawyer? The extending artificial intelligence robot lawyer technology acceptance model for legal industry by an exploratory study

Wang

2019

Journal of Management & Organization

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The development of artificial intelligence has created new opportunities and challenges in industries. The competition between robots and humans has elicited extensive attention among legal researchers. In this exploratory study, we addressed issues regarding the introduction of robots to the practice of legal service through a semistructured interviews with lawyers, judges, artificial intelligence experts, and potential clients. An extended robot lawyer technology acceptance model with five facets and 11 elements is proposed in this study. This model highlights two dimensions: ‘legal use’ and ‘perception of trust.’ In summary, this study provides new specific implications and exhibits three characteristics, namely, derivative, macroscopic, and instructive, in the legal services with artificial intelligence. In addition, artificial intelligence robot lawyers are being developed with some of the abilities necessary to substitute for human beings. Nevertheless, working with human lawyers is imperative to produce benefits from this type of reciprocity.

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Section: Irreplaceable Human Abilitiesmentioning

confidence: 97%

Adopting robot lawyer? The extending artificial intelligence robot lawyer technology acceptance model for legal industry by an exploratory study

Wang

2019

Journal of Management & Organization

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“…Various strategies for managing computational models of episodic memory are available to memorize experience sequences [37], [38], [39]. Hierarchical emotional episodic memory, using deep adaptive resonance theory network [40] has also been proposed to learn emotions correlated with past experiences. Generalized fusion adaptive resonance theory-based EM-ART has been introduced to make the episodic memory suitable to handle intricate relations of past events [41], [42].…”

Section: Related Workmentioning

confidence: 99%

Episodic Memory Model for Learning Robotic Manipulation Tasks

Behbahani,

Chhatpar,

Zahrai

et al. 2021

Preprint

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Machine learning, artificial intelligence and especially deep learning based approaches are often used to simplify or eliminate the burden of programming industrial robots. Using these approaches robots inherently learn a skill instead of being programmed using strict and tedious programming instructions. While deep learning is effective in making robots learn skills, it does not offer a practical route for teaching a complete task, such as assembly or machine tending, where a complex logic must be understood and related sub-tasks need to be performed. We present a model similar to an episodic memory that allows robots to comprehend sequences of actions using single demonstration and perform them properly and accurately. The algorithm identifies and recognizes the changes in the states of the system and memorizes how to execute the necessary tasks in order to make those changes. This allows the robot to decompose the tasks into smaller sub-tasks, retain the essential steps, and remember how they have been performed.

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“…Mybot includes a robotic head connected to the upper body through a three degrees of freedom (DoFs) neck, RGB-D camera, two arms (10 DoFs for each) attached to the upper body, one 2-DoFs trunk, and an omnidirectional wheel-base with a power supply. Mybot is able to perform home chore tasks [30], control gaze [31], and converse autonomously [32]. We utilized previously built modules in the Mybot system to implement the demonstration scenario.…”

Section: B Applicability Demonstration 1) Environmentmentioning

confidence: 99%

3-D Scene Graph: A Sparse and Semantic Representation of Physical Environments for Intelligent Agents

Kim

Park

Song

et al. 2020

IEEE Trans. Cybern.

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Intelligent agents gather information and perceive semantics within the environments before taking on given tasks. The agents store the collected information in the form of environment models that compactly represent the surrounding environments. The agents, however, can only conduct limited tasks without an efficient and effective environment model. Thus, such an environment model takes a crucial role for the autonomy systems of intelligent agents. We claim the following characteristics for a versatile environment model: accuracy, applicability, usability, and scalability. Although a number of researchers have attempted to develop such models that represent environments precisely to a certain degree, they lack broad applicability, intuitive usability, and satisfactory scalability. To tackle these limitations, we propose 3-D scene graph as an environment model and the 3-D scene graph construction framework. The concise and widely used graph structure readily guarantees usability as well as scalability for 3-D scene graph. We demonstrate the accuracy and applicability of the 3-D scene graph by exhibiting the deployment of the 3-D scene graph in practical applications. Moreover, we verify the performance of the proposed 3-D scene graph and the framework by conducting a series of comprehensive experiments under various conditions.

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Hierarchical emotional episodic memory for social human robot collaboration

Cited by 16 publications

References 27 publications

Adopting robot lawyer? The extending artificial intelligence robot lawyer technology acceptance model for legal industry by an exploratory study

Adopting robot lawyer? The extending artificial intelligence robot lawyer technology acceptance model for legal industry by an exploratory study

Episodic Memory Model for Learning Robotic Manipulation Tasks

3-D Scene Graph: A Sparse and Semantic Representation of Physical Environments for Intelligent Agents

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