Learning social relations for culture aware interaction

Patompak, Pakpoom; Jeong, Sungmoon; Nilkhamhang, Itthisek; Chong, Nak Young

doi:10.1109/urai.2017.7992879

Cited by 5 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The algorithm by Luber and Arras [168] was extended in [160] for detecting and learning sociospatial relations, which are used for creating a social network graph to track groups of humans. Patompak et al [231] developed a Reinforcement Learning method of estimating a social interaction model for assisting the navigation algorithm regarding social relations between humans in the robot's environment model. Similarly, Okal and Arras [232] employed Bayesian Inverse Reinforcement Learning for learning the cost function of traversing in the area with a group of humans.…”

Section: Interpersonal Contextmentioning

confidence: 99%

Bridging Requirements, Planning, and Evaluation: A Review of Social Robot Navigation

Karwowski,

Szynkiewicz,

Niewiadomska-Szynkiewicz

2024

Sensors

View full text Add to dashboard Cite

Navigation lies at the core of social robotics, enabling robots to navigate and interact seamlessly in human environments. The primary focus of human-aware robot navigation is minimizing discomfort among surrounding humans. Our review explores user studies, examining factors that cause human discomfort, to perform the grounding of social robot navigation requirements and to form a taxonomy of elementary necessities that should be implemented by comprehensive algorithms. This survey also discusses human-aware navigation from an algorithmic perspective, reviewing the perception and motion planning methods integral to social navigation. Additionally, the review investigates different types of studies and tools facilitating the evaluation of social robot navigation approaches, namely datasets, simulators, and benchmarks. Our survey also identifies the main challenges of human-aware navigation, highlighting the essential future work perspectives. This work stands out from other review papers, as it not only investigates the variety of methods for implementing human awareness in robot control systems but also classifies the approaches according to the grounded requirements regarded in their objectives.

show abstract

Section: Interpersonal Contextmentioning

confidence: 99%

Bridging Requirements, Planning, and Evaluation: A Review of Social Robot Navigation

Karwowski,

Szynkiewicz,

Niewiadomska-Szynkiewicz

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…The fact that the same parameter might have different preferred values in different situations (e.g., someone living in a condominium might want the robot to lower its Volume in the evening not to disturb the neighbours) is modelled by building a collection of subclasses below the parameter, with one class per situation of relevance (e.g., VolumeEvening). The taxonomy of subclasses corresponding to different situations and their initial values are defined by experts 2 and revised through interaction [27].…”

Section: Robot Domainmentioning

confidence: 99%