Robot social-aware navigation framework to accompany people walking side-by-side

Ferrer, Gonzalo; Zulueta, Anais Garrell; Cotarelo, Fernando Herrero; Sanfeliu, Alberto

doi:10.1007/s10514-016-9584-y

Cited by 87 publications

(74 citation statements)

References 46 publications

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“…Previous researchers have proposed many methods to solve the navigation problem. The Social Force Model [19] is one of the representative methods that has been successfully applied and extended in different environments [7], [20], [21]. Reciprocal velocity obstacles (RVO), which consider communications with other agents, were proposed in multiagent navigation scenarios [2].…”

Section: A Robot Navigation In Crowdsmentioning

confidence: 99%

Robot Navigation in Crowds by Graph Convolutional Networks With Attention Learned From Human Gaze

Chen

Liu

Shi

et al. 2020

IEEE Robot. Autom. Lett.

120

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Safe and efficient crowd navigation for mobile robot is a crucial yet challenging task. Previous work has shown the power of deep reinforcement learning frameworks to train efficient policies. However, their performance deteriorates when the crowd size grows. We suggest that this can be addressed by enabling the network to identify and pay attention to the humans in the crowd that are most critical to navigation. We propose a novel network utilizing a graph representation to learn the policy. We first train a graph convolutional network based on human gaze data that accurately predicts human attention to different agents in the crowd. Then we incorporate the learned attention into a graph-based reinforcement learning architecture. The proposed attention mechanism enables the assignment of meaningful weightings to the neighbors of the robot, and has the additional benefit of interpretability. Experiments on real-world dense pedestrian datasets with various crowd sizes demonstrate that our model outperforms state-ofart methods by 18.4% in task accomplishment and by 16.4% in time efficiency.

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Section: A Robot Navigation In Crowdsmentioning

confidence: 99%

Robot Navigation in Crowds by Graph Convolutional Networks With Attention Learned From Human Gaze

Chen

Liu

Shi

et al. 2020

IEEE Robot. Autom. Lett.

120

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“…Helbing and Molar [9] presented the Social Force Model (SFM), where they modeled the assumed social forces governing the human motion. Ferrer et al [6] used the social force model to navigate in a way similar to humans. In their work, the robot navigates to the target while abiding by the social forces, that is, the robot is attracted by its target and repelled by pedestrians and obstacles.…”

Section: Background and State Of The Artmentioning

confidence: 99%

“…EMP is trained to learn a navigation algorithm. 3) Social Force Model (SFM): SFM calculates a set of imaginary 'social' forces that govern the human motion in a crowd [6]. These forces can be grouped as repulsive to obstacles and other humans as well as attractive to the target.…”

Section: A Benchmarksmentioning

confidence: 99%

DeepMoTIon: Learning to Navigate Like Humans

Hamandi

D’Arcy

Fazli

2019

2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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We present a novel human-aware navigation approach, where the robot learns to mimic humans to navigate safely in crowds. The presented model, referred to as Deep-MoTIon, is trained with pedestrian surveillance data to predict human velocity in the environment. The robot processes LiDAR scans via the trained network to navigate to the target location. We conduct extensive experiments to assess the components of our network and prove their necessity to imitate humans. Our experiments show that DeepMoTIion outperforms all the benchmarks in terms of human imitation, achieving a 24% reduction in time series-based path deviation over the next best approach. In addition, while many other approaches often failed to reach the target, our method reached the target in 100% of the test cases while complying with social norms and ensuring human safety.

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“…Human-Robot Collaborative Navigation One of the first faced humanrobot collaborative challenges was side-by-side navigation [18,19]. In parallel, [8,9,11] approached this challenge through Social Force Model (SFM) methods and, in another context, [23,16] presented methods for side-by-side wheelchair navigation. Alternative approaches to HRCN include co-driving, as the collaborative teleoperation of a robot through dialogue [10] or the collaborative control of wheelchair [4].…”

Section: Human-robot Collaborationmentioning

confidence: 99%

Human-Robot Collaborative Navigation Search Using Social Reward Sources

Dalmasso

Garrell

Jiménez

et al. 2019

Advances in Intelligent Systems and Computing

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This paper proposes a Social Reward Sources (SRS) design for a Human-Robot Collaborative Navigation (HRCN) task: humanrobot collaborative search. It is a flexible approach capable of handling the collaborative task, human-robot interaction and environment restrictions, all integrated on a common environment. We modelled task rewards based on unexplored area observability and isolation and evaluated the model through different levels of human-robot communication. The models are validated through quantitative evaluation against both agents' individual performance and qualitative surveying of participants' perception. After that, the three proposed communication levels are compared against each other using the previous metrics.

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Robot social-aware navigation framework to accompany people walking side-by-side

Cited by 87 publications

References 46 publications

Robot Navigation in Crowds by Graph Convolutional Networks With Attention Learned From Human Gaze

Robot Navigation in Crowds by Graph Convolutional Networks With Attention Learned From Human Gaze

DeepMoTIon: Learning to Navigate Like Humans

Human-Robot Collaborative Navigation Search Using Social Reward Sources

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