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Impact of Trajectory Generation Methods on Viewer perception of Robot Approaching Group Behaviors

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Impact of Trajectory Generation Methods on Viewer Perception...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Fangkai Yang Wenjie Yin Mårten Björkman Christopher Peters Department of Computational Science and Technology KTH Royal Institute of Technology Stockholm Sweden Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

Mobile robots that approach free-standing conversational groups to join them should behave in a safe and socially-acceptable way. Existing trajectory generation methods focus on collision avoidance with pedestrians, and the models that generate approach behaviors into groups are evaluated in simulation. However, it is challenging to generate approach and join trajectories that avoid collisions with group members while also ensuring that they do not invoke feelings of discomfort. In this paper, we conducted an experiment to examine the impact of three trajectory generation methods for a mobile robot to approach groups from multiple directions: a Wizard-of-Oz (WoZ) method, a procedural social-aware navigation model (PM) and a novel generative adversarial model imitating human approach behaviors (IL). Measures also compared two camera viewpoints and static versus quasi-dynamic groups. The latter refers to a group whose members change orientation and position throughout the approach task, even though the group entity remains static in the environment. This represents a more realistic but challenging scenario for the robot. We evaluate three methods with objective measurements and subjective measurements from viewer perception, and results show that WoZ and IL have comparable performance, and both perform better than PM under most conditions.

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Sharing Pain: Using Pain Domain Transfer for Video Recognition of Low Grade Orthopedic Pain in Horses

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arXiv 2021年

作者： Broomé, Sofia Ask, Katrina Rashid, Maheen Andersen, Pia Haubro Kjellström, Hedvig Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden Department of Anatomy Physiology and Biochemistry Swedish University of Agricultural Sciences Uppsala Sweden Department of Clinical Sciences Swedish University of Agricultural Sciences Uppsala Sweden Department of Computer Science University of California Davis United States Univrses Stockholm Sweden Stockholm Sweden

Orthopedic disorders are common among horses, often leading to euthanasia, which often could have been avoided with earlier detection. These conditions often create varying degrees of subtle long-term pain. It is challenging to train a visual pain recognition method with video data depicting such pain, since the resulting pain behavior also is subtle, sparsely appearing, and varying, making it challenging for even an expert human labeller to provide accurate ground-truth for the data. We show that a model trained solely on a dataset of horses with acute experimental pain (where labeling is less ambiguous) can aid recognition of the more subtle displays of orthopedic pain. Moreover, we present a human expert baseline for the problem, as well as an extensive empirical study of various domain transfer methods and of what is detected by the pain recognition method trained on clean experimental pain in the orthopedic dataset. Finally, this is accompanied with a discussion around the challenges posed by real-world animal behavior datasets and how best practices can be established for similar fine-grained action recognition tasks. Our code is available at https://***/sofiabroome/painface-recognition. Copyright © 2021, The Authors. All rights reserved.

关键词： Health

Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States

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arXiv 2022年

作者： Broomé, Sofia Feighelstein, Marcelo Zamansky, Anna Lencioni, Gabriel Carreira Andersen, Pia Haubro Pessanha, Francisca Mahmoud, Marwa Kjellström, Hedvig Salah, Albert Ali Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden Tech4Animals Lab Information Systems Department University of Haifa Haifa Israel Department of Preventive Veterinary Medicine and Animal Health School of Veterinary Medicine and Animal Science University of São Paulo SP São Paulo Brazil School of Computing Science University of Glasgow Glasgow United Kingdom Silo AI Stockholm Sweden Department of Clinical Sciences Swedish University of Agricultural Sciences Uppsala Sweden Department of Information and Computing Sciences Universiteit Utrecht Utrecht Netherlands Boğaziçi University Istanbul Turkey

Advances in animal motion tracking and pose recognition have been a game changer in the study of animal behavior. Recently, an increasing number of works go ‘deeper’ than tracking, and address automated recognition of animals’ internal states such as emotions and pain with the aim of improving animal welfare, making this a timely moment for a systematization of the field. This paper provides a comprehensive survey of computer vision-based research on recognition of affective states and pain in animals, addressing both facial and bodily behavior analysis. We summarize the efforts that have been presented so far within this topic – classifying them across different dimensions, highlight challenges and research gaps, and provide best practice recommendations for advancing the field, and some future directions for research. Copyright © 2022, The Authors. All rights reserved.

关键词： Computer vision

Grapevine Winter pruning automation: On potential pruning points detection through 2D plant modeling using grapevine segmentation

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arXiv 2021年

作者： Fernandes, Miguel Scaldaferri, Antonello Fiameni, Giuseppe Teng, Tao Gatti, Matteo Poni, Stefano Semini, Claudio Caldwell, Darwin Chen, Fei Active Perception and Robot Interactive Learning Laboratory Department of Advanced Robotics Istituto Italiano di Tecnologia Via Morego 30 Genova16163 Italy Department of Informatics Bioengineering Robotics and System Engineering Università di Genoa Viale Causa 13 Genova16145 Italy Italy Department of Sustainable Crop Production Università Cattolica del Sacro Cuore Via Emilia Parmense 84 Piacenza29122 Italy lab Istituto Italiano di Tecnologia Via Morego 30 Genova16163 Italy Department of Mechanical and Automation Engineering T-Stone Robotics Institute Chinese University of Hong Kong Chung Chi Rd Ma Liu Shui Hong Kong

Grapevine winter pruning is a complex task, that requires skilled workers to execute it correctly. The complexity of this task is also the reason why it is time consuming. Considering that this operation takes about 80-120 hours/ha to be completed, and therefore is even more crucial in large-size vineyards, an automated system can help to speed up the process. To this end, this paper presents a novel multidisciplinary approach that tackles this challenging task by performing object segmentation on grapevine images, used to create a representative model of the grapevine plants. Second, a set of potential pruning points is generated from this plant representation. We will describe (a) a methodology for data acquisition and annotation, (b) a neural network fine-tuning for grapevine segmentation, (c) an image processing based method for creating the representative model of grapevines, starting from the inferred segmentation and (d) potential pruning points detection and localization, based on the plant model which is a simplification of the grapevine structure. With this approach, we are able to identify a significant set of potential pruning points on the canes, that can be used, with further selection, to derive the final set of the real pruning points. © 2021, CC BY.

关键词： Automation

Towards precise pruning points detection using semantic-instance-aware plant models for grapevine winter pruning automation

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arXiv 2021年

作者： Fernandes, Miguel Scaldaferri, Antonello Guadagna, Paolo Fiameni, Giuseppe Teng, Tao Gatti, Matteo Poni, Stefano Semini, Claudio Caldwell, Darwin Chen, Fei Active Perception and Robot Interactive Learning Laboratory Department of Advanced Robotics Istituto Italiano di Tecnologia Via Morego 30 Genova16163 Italy Department of Informatics Bioengineering Robotics and System Engineering Università di Genoa Viale Causa 13 Genova16145 Italy Department of Sustainable Crop Production Università Cattolica del Sacro Cuore Via Emilia Parmense 84 Piacenza29122 Italy Italy lab Istituto Italiano di Tecnologia Via Morego 30 Genova16163 Italy Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Chung Chi Rd Ma Liu Shui Hong Kong

Grapevine winter pruning is a complex task, that requires skilled workers to execute it correctly. The complexity makes it time consuming. It is an operation that requires about 80-120 hours per hectare annually, making an automated robotic system that helps in speeding up the process a crucial tool in large-size vineyards. We will describe (a) a novel expert annotated dataset for grapevine segmentation, (b) a state of the art neural network implementation and (c) generation of pruning points following agronomic rules, leveraging the simplified structure of the plant. With this approach, we are able to generate a set of pruning points on the canes, paving the way towards a correct automation of grapevine winter pruning. © 2021, CC BY.

关键词： Automation

Fully convolutional networks for velocity-field predictions based on the wall heat flux in turbulent boundary layers

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arXiv 2022年

作者： Guastoni, Luca Balasubramanian, Arivazhagan G. Foroozan, Firoozeh Güemes, Alejandro Ianiro, Andrea Discetti, Stefano Schlatter, Philipp Azizpour, Hossein Vinuesa, Ricardo FLOW Engineering Mechanics KTH Royal Institute of Technology Osquars Backe 18 Stockholm114 28 Sweden Osquars Backe 18 Stockholm114 28 Sweden Department of Aerospace Engineering Universidad Carlos III de Madrid Avda. de la Universidad 30 Leganés Madrid28911 Spain Institute of Fluid Mechanics Friedrich-Alexander Universität Cauerstraße 4 Erlangen-Nürnberg91058 Germany Division of Robotics Perception and Learning KTH Royal Institute of Technology Lindstedtsvägen 30 Stockholm114 28 Sweden

Fully-convolutional neural networks (FCN) were proven to be effective for predicting the instantaneous state of a fully-developed turbulent flow at different wall-normal locations using quantities measured at the wall. In Guastoni et al. (J. Fluid Mech. 2021, 928, A27), we focused on wall-shear-stress distributions as input, which are difficult to measure in experiments. In order to overcome this limitation, we introduce a model that can take as input the heat-flux field at the wall from a passive scalar. Four different Prandtl numbers Pr = ν/α = (1, 2, 4, 6) are considered (where ν is the kinematic viscosity and α is the thermal diffusivity of the scalar quantity). A turbulent boundary layer is simulated since accurate heat-flux measurements can be performed in experimental settings: first we train the network on aptly-modified DNS data and then we fine-tune it on the experimental data. Finally, we test our network on experimental data sampled in a water tunnel. These predictions represent the first application of transfer learning on experimental data of neural networks trained on simulations. This paves the way for the implementation of a non-intrusive sensing approach for the flow in practical applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

learning to Estimate Pose and Shape of Hand-Held Objects from RGB Images

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Learning to Estimate Pose and Shape of Hand-Held Objects fro...

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Mia Kokic Danica Kragic Jeannette Bohg Robotics Perception and Learning EECS KTH Stockholm Sweden Computer Science Department Stanford University CA USA

We develop a system for modeling hand-object interactions in 3D from RGB images that show a hand which is holding a novel object from a known category. We design a Convolutional Neural Network (CNN) for Hand-held Object Pose and Shape estimation called HOPS-Net and utilize prior work to estimate the hand pose and configuration. We leverage the insight that information about the hand facilitates object pose and shape estimation by incorporating the hand into both training and inference of the object pose and shape as well as the refinement of the estimated pose. The network is trained on a large synthetic dataset of objects in interaction with a human hand. To bridge the gap between real and synthetic images, we employ an image-to-image translation model (Augmented CycleGAN) that generates realistically textured objects given a synthetic rendering. This provides a scalable way of generating annotated data for training HOPS-Net. Our quantitative experiments show that even noisy hand parameters significantly help object pose and shape estimation. The qualitative experiments show results of pose and shape estimation of objects held by a hand “in the wild”.

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Loop Closure Detection Through Environmental Indicators In Underwater SLAM

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Loop Closure Detection Through Environmental Indicators In U...

OCEANS - Europe

作者： Ignacio Torroba Nils Bore Anna Wåhlin John Folkesson Robotics Perception and Learning Division KTH Stockholm Sweden Department of Marine Sciences University of Gothenburg Gothenburg Sweden

A key step in Simultaneous Localization and Mapping (SLAM) with autonomous robots is the ability to recognize when the vehicle is revisiting an area. The recognition of salient features allows re-localization to a previously visited place, forming a so-called loop closure. Loop closures enable autonomous underwater vehicles (AUVs) to reduce the unbounded navigation drift experienced when mapping unknown terrain. However, the scarcity of recognizable features in unstructured sub-sea environments makes disambiguation of places a challenging problem. In this work we study the application of water analysis sensors mounted on an AUV to construct descriptors of underwater environments. Since these sensors measure very different properties of the areas compared to terrain sensors, these descriptors could increase robustness against perceptual aliasing on place recognition and thus improve loop closure detection in a SLAM framework. We present a method to create and compare these spatial descriptors, test it with several data sets collected with an AUV in two different geographical locations and analyze their potential use for loop closure detection.

关键词： Simultaneous localization and mapping Trajectory Payloads Biosensors

learning to Estimate Pose and Shape of Hand-Held Objects from RGB Images

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arXiv 2019年

作者： Kokic, Mia Kragic, Danica Bohg, Jeannette Division of Robotics Perception and Learning Eecs Kth Stockholm Sweden Computer Science Department Stanford University CA United States

We develop a system for modeling hand-object interactions in 3D from RGB images that show a hand which is holding a novel object from a known category. We design a Convolutional Neural Network (CNN) for Hand-held Object Pose and Shape estimation called HOPS-Net and utilize prior work to estimate the hand pose and configuration. We leverage the insight that information about the hand facilitates object pose and shape estimation by incorporating the hand into both training and inference of the object pose and shape as well as the refinement of the estimated pose. The network is trained on a large synthetic dataset of objects in interaction with a human hand. To bridge the gap between real and synthetic images, we employ an image-To-image translation model (Augmented CycleGAN) that generates realistically textured objects given a synthetic rendering. This provides a scalable way of generating annotated data for training HOPS-Net. Our quantitative experiments show that even noisy hand parameters significantly help object pose and shape estimation. The qualitative experiments show results of pose and shape estimation of objects held by a hand "in the wild". Copyright © 2019, The Authors. All rights reserved.

关键词： Textures