DeepMoTIon: Learning to Navigate Like Humans

Hamandi, Mahmoud; D’Arcy, Mike; Fazli, Pooyan

doi:10.1109/ro-man46459.2019.8956408

Cited by 11 publications

(9 citation statements)

References 23 publications

(35 reference statements)

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“…Contrary to supervised learning, IRL is able to recover a cost function that explains an observed behavior (Kuderer et al, 2013 ). The IRL technique proposed by Hamandi et al ( 2019 ) trains the social navigation model by learning the navigation policy directly from human navigated paths in order to generate actions that conform to human-like trajectories. To include the social context in the learning process, these models aim to clone the navigation behavior of humans.…”

Section: Learning—relearning Framework For Socially-aware Robot Namentioning

confidence: 99%

From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation

2021

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The exponentially increasing advances in robotics and machine learning are facilitating the transition of robots from being confined to controlled industrial spaces to performing novel everyday tasks in domestic and urban environments. In order to make the presence of robots safe as well as comfortable for humans, and to facilitate their acceptance in public environments, they are often equipped with social abilities for navigation and interaction. Socially compliant robot navigation is increasingly being learned from human observations or demonstrations. We argue that these techniques that typically aim to mimic human behavior do not guarantee fair behavior. As a consequence, social navigation models can replicate, promote, and amplify societal unfairness, such as discrimination and segregation. In this work, we investigate a framework for diminishing bias in social robot navigation models so that robots are equipped with the capability to plan as well as adapt their paths based on both physical and social demands. Our proposed framework consists of two components: learning which incorporates social context into the learning process to account for safety and comfort, and relearning to detect and correct potentially harmful outcomes before the onset. We provide both technological and societal analysis using three diverse case studies in different social scenarios of interaction. Moreover, we present ethical implications of deploying robots in social environments and propose potential solutions. Through this study, we highlight the importance and advocate for fairness in human-robot interactions in order to promote more equitable social relationships, roles, and dynamics and consequently positively influence our society.

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Section: Learning—relearning Framework For Socially-aware Robot Namentioning

confidence: 99%

From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation

2021

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“…Dynamic Time Warping (DTW) used in [87] finds the optimal way to warp prediction to a ground truth path, and considers the cost of performing such a warping as the difference between prediction and truth. In the similar vein, the Hausdorff distance or Fréchet distance can also be considered.…”

Section: Behavioral Naturalnessmentioning

confidence: 99%

Core Challenges of Social Robot Navigation: A Survey

Mavrogiannis¹,

Baldini²,

Wang³

et al. 2021

Preprint

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Robot navigation in crowded public spaces is a complex task that requires addressing a variety of engineering and human factors challenges. These challenges have motivated a great amount of research resulting in important developments for the fields of robotics and human-robot interaction over the past three decades. Despite the significant progress and the massive recent interest, we observe a number of significant remaining challenges that prohibit the seamless deployment of autonomous robots in public pedestrian environments. In this survey article, we organize existing challenges into a set of categories related to broader open problems in motion planning, behavior design, and evaluation methodologies. Within these categories, we review past work, and offer directions for future research. Our work builds upon and extends earlier survey efforts by a) taking a critical perspective and diagnosing fundamental limitations of adopted practices in the field and b) offering constructive feedback and ideas that we aspire will drive research in the field over the coming decade.

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“…The architecture consists of a Convolutional Neural Network (CNN) part that extracts information about the input laser data and Fully Connected layers that combine extracted feature maps and target positions to generate the required navigation commands. Similarly, Hamandi et al [10] processed the information of LiDAR scans to predict the speed and direction for robots by a novel Deep Neural Network (DNN) model that utilises Long Short-Term Memory (LSTM) layers and adopts ResNet [11] as the backbone.…”

Section: End-to-end Algorithmsmentioning

confidence: 99%

A survey of learning‐based robot motion planning

Wang

Zhang

et al. 2021

IET Cyber-Syst and Robotics

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A fundamental task in robotics is to plan collision-free motions among a set of obstacles. Recently, learning-based motion-planning methods have shown significant advantages in solving different planning problems in high-dimensional spaces and complex environments. This article serves as a survey of various different learning-based methods that have been applied to robot motion-planning problems, including supervised, unsupervised learning, and reinforcement learning. These learning-based methods either rely on a human-crafted reward function for specific tasks or learn from successful planning experiences. The classical definition and learning-related definition of motion-planning problem are provided in this article. Different learning-based motion-planning algorithms are introduced, and the combination of classical motion-planning and learning techniques is discussed in detail.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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DeepMoTIon: Learning to Navigate Like Humans

Cited by 11 publications

References 23 publications

From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation

From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation

Core Challenges of Social Robot Navigation: A Survey

A survey of learning‐based robot motion planning

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