Modelling Multi-Channel Emotions Using Facial Expression and Trajectory Cues for Improving Socially-Aware Robot Navigation

Bera, Aniket; Randhavane, Tanmay; Manocha, Dinesh

doi:10.1109/cvprw.2019.00035

Cited by 10 publications

(4 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, Bera et al [26] attempted to classify the personality of each pedestrian in the crowd to differentiate the sizes of personal spaces of individuals. Subsequently, the emotional state of the pedestrians was also inferred and embedded for socially aware navigation [27,241,242].…”

Section: Diversity 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: Diversity 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

“…Understanding human impressions of robot performance is important. They can be used to evaluate robot policies [15]- [17] and to create better robot behavior [7], [18]- [20], increasing the likelihood of robot adoption. In this work, we focus on inferring three robot performance dimensions relevant to navigation [12]: robot competence, the surprisingness of robot behavior, and clear intent.…”

Section: Related Workmentioning

confidence: 99%

Recent Advances in Mobile Robot Localization in Complex Scenarios

Zhang

Liu

et al. 2023

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Human impressions of robot performance are often measured through surveys. As a more scalable and cost-effective alternative, we study the possibility of predicting people's impressions of robot behavior using non-verbal behavioral cues and machine learning techniques. To this end, we first contribute the SEAN TOGETHER Dataset consisting of observations of an interaction between a person and a mobile robot in a Virtual Reality simulation, together with impressions of robot performance provided by users on a 5-point scale. Second, we contribute analyses of how well humans and supervised learning techniques can predict perceived robot performance based on different combinations of observation types (e.g., facial, spatial, and map features). Our results show that facial expressions alone provide useful information about human impressions of robot performance; but in the navigation scenarios we tested, spatial features are the most critical piece of information for this inference task. Also, when evaluating results as binary classification (rather than multiclass classification), the F1-Score of human predictions and machine learning models more than doubles, showing that both are better at telling the directionality of robot performance than predicting exact performance ratings. Based on our findings, we provide guidelines for implementing these predictions models in real-world navigation scenarios.

show abstract

“…Bera et al [111] proposed an emotion-aware navigation algorithm for social robots which combined emotions learned from facial expressions and walking trajectories using an onboard and an overhead camera respectively. The approach achieved accurate emotion detection and enabled socially conscious robot navigation in low-to-medium-density environments.…”

Section: Inferencementioning

confidence: 99%

Perception for Humanoid Robots

Roychoudhury,

Khorshidi,

Agrawal

et al. 2023

Curr Robot Rep

View full text Add to dashboard Cite

Purpose of Review The field of humanoid robotics, perception plays a fundamental role in enabling robots to interact seamlessly with humans and their surroundings, leading to improved safety, efficiency, and user experience. This scientific study investigates various perception modalities and techniques employed in humanoid robots, including visual, auditory, and tactile sensing by exploring recent state-of-the-art approaches for perceiving and understanding the internal state, the environment, objects, and human activities. Recent Findings Internal state estimation makes extensive use of Bayesian filtering methods and optimization techniques based on maximum a-posteriori formulation by utilizing proprioceptive sensing. In the area of external environment understanding, with an emphasis on robustness and adaptability to dynamic, unforeseen environmental changes, the new slew of research discussed in this study have focused largely on multi-sensor fusion and machine learning in contrast to the use of hand-crafted, rule-based systems. Human robot interaction methods have established the importance of contextual information representation and memory for understanding human intentions. Summary This review summarizes the recent developments and trends in the field of perception in humanoid robots. Three main areas of application are identified, namely, internal state estimation, external environment estimation, and human robot interaction. The applications of diverse sensor modalities in each of these areas are considered and recent significant works are discussed.

show abstract

Modelling Multi-Channel Emotions Using Facial Expression and Trajectory Cues for Improving Socially-Aware Robot Navigation

Cited by 10 publications

References 35 publications

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

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

Recent Advances in Mobile Robot Localization in Complex Scenarios

Perception for Humanoid Robots

Contact Info

Product

Resources

About