Perception-Action-Learning System for Mobile Social-Service Robots Using Deep Learning

Lee, Beom‐Jin; Choi, Jin-Young; Lee, Chung-Yeon; Park, Kyung-Wha; Choi, Sungjun; Han, Cheolho; Han, Dong-Sig; Baek, Christina; Emaase, Patrick Mokodir; Zhang, Byoung-Tak

doi:10.1609/aaai.v32i1.11367

Cited by 5 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This allowed the design and development of more complex robots [28] and control architectures based on how the human brain processes [5] to improve robotic capabilities to perform specific tasks. However, when it comes to include the human in-the-loop, authors recognize that it is not enough to obtain human-like robots [22]. That is why we extended it including the human's intention.…”

Section: Related Workmentioning

confidence: 99%

Perception-Intention-Action Cycle as a Human Acceptable Way for Improving Human-Robot Collaborative Tasks

Domínguez-Vidal

Rodríguez

Sanfeliu

2023

Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction

View full text Add to dashboard Cite

In Human-Robot Collaboration (HRC) tasks, the classical Perception-Action cycle can not fully explain the collaborative behaviour of the human-robot pair until it is extended to Perception-Intention-Action (PIA) cycle, giving to the human's intention a key role at the same level of the robot's perception and not as a subblock of this. Although part of the human's intention can be perceived or inferred by the other agent, this is prone to misunderstandings so the true intention has to be explicitly informed in some cases to fulfill the task. Here, we explore both types of intention and we combine them with the robot's perception through the concept of Situation Awareness (SA). We validate the PIA cycle and its acceptance by the user with a preliminary experiment in an object transportation task showing that its usage can increase trust in the robot. CCS CONCEPTS• Human-centered computing → Collaborative interaction; Empirical studies in HCI .

show abstract

Section: Related Workmentioning

confidence: 99%

Perception-Intention-Action Cycle as a Human Acceptable Way for Improving Human-Robot Collaborative Tasks

Domínguez-Vidal

Rodríguez

Sanfeliu

2023

Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction

View full text Add to dashboard Cite

show abstract

“…This allowed the design and development of more complex robots [9] and control architectures based on how the human brain processes [10] to improve robotic capabilities to perform specific tasks. However, when it comes to include the human in-the-loop, authors recognize that it is not enough to obtain human-like robots [11]. That is why we extended it including the human's intention.…”

Section: Related Workmentioning

confidence: 99%

Perception-Intention-Action Cycle in Human-Robot Collaborative Tasks

Domínguez-Vidal¹,

Rodríguez²,

Alquézar³

et al. 2022

Preprint

View full text Add to dashboard Cite

In this work we argue that in Human-Robot Collaboration (HRC) tasks, the Perception-Action cycle in HRC tasks can not fully explain the collaborative behaviour of the human and robot and it has to be extended to Perception-Intention-Action cycle, where Intention is a key topic. In some cases, agent Intention can be perceived or inferred by the other agent, but in others, it has to be explicitly informed to the other agent to succeed the goal of the HRC task. The Perception-Intention-Action cycle includes three basic functional procedures: Perception-Intention, Situation Awareness and Action. The Perception and the Intention are the input of the Situation Awareness, which evaluates the current situation and projects it, into the future situation. The agents receive this information, plans and agree with the actions to be executed and modify their action roles while perform the HRC task. In this work, we validate the Perception-Intention-Action cycle in a joint object transportation task, modeling the Perception-Intention-Action cycle through a force model which uses real life and social forces. The perceived world is projected into a force world and the human intention (perceived or informed) is also modelled as a force that acts in the HRC task. Finally, we show that the action roles (master-slave, collaborative, neutral or adversary) are intrinsic to any HRC task and they appear in the different steps of a collaborative sequence of actions performed during the task.

show abstract

“…Humans are often at the centre of such interactions and detecting human actions is an important practical and scientific problem. In order to develop more dynamic virtual worlds and smarter robots [1], it is necessary to teach machines to capture, understand and replicate these interactions. The information that is need to be learnt is widely available in the form of large video collections.…”

Section: Introductionmentioning

confidence: 99%

Abnormality Detection in Human Action Using Thermal Videos

Soundararajan

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

View full text Add to dashboard Cite

Anomaly detection is a challenging task in the surveillance system due to the factors like extracting appropriate features, inappropriate differentiation among the normal vs abnormal behaviours, the sparse occurrence of abnormal activities and environmental variations. In the dark environment, detection of human actions is still difficult as more features for recognizing the key point are not visible. Hence the proposed work is focused on overcoming the environmental variations task that too in a less bright environment by using thermal videos. Variations in the actions can be easily identified as it works on the property of infrared radiations. For recognizing actions, the skeleton-based approach is used as it helps with the joint-wise segregation of human parts, resulting in more accuracy. The motion pattern of humans in the thermal video is tracked to classify the level of abnormality.

show abstract

Perception-Action-Learning System for Mobile Social-Service Robots Using Deep Learning

Cited by 5 publications

References 4 publications

Perception-Intention-Action Cycle as a Human Acceptable Way for Improving Human-Robot Collaborative Tasks

Perception-Intention-Action Cycle as a Human Acceptable Way for Improving Human-Robot Collaborative Tasks

Perception-Intention-Action Cycle in Human-Robot Collaborative Tasks

Abnormality Detection in Human Action Using Thermal Videos

Contact Info

Product

Resources

About