Pub Date : 2019-05-20DOI: 10.1109/ICRA.2019.8794462
Fabian Schenk, F. Fraundorfer
Simultaneous Localization and Mapping is a key requirement for many practical applications in robotics. In this work, we present RESLAM, a novel edge-based SLAM system for RGBD sensors. Due to their sparse representation, larger convergence basin and stability under illumination changes, edges are a promising alternative to feature-based or other direct approaches. We build a complete SLAM pipeline with camera pose estimation, sliding window optimization, loop closure and relocalisation capabilities that utilizes edges throughout all steps. In our system, we additionally refine the initial depth from the sensor, the camera poses and the camera intrinsics in a sliding window to increase accuracy. Further, we introduce an edge-based verification for loop closures that can also be applied for relocalisation. We evaluate RESLAM on wide variety of benchmark datasets that include difficult scenes and camera motions and also present qualitative results. We show that this novel edge-based SLAM system performs comparable to state-of-the-art methods, while running in real-time on a CPU. RESLAM is available as open-source software1.1Code is available: https://github.com/fabianschenk/RESLAM
{"title":"RESLAM: A real-time robust edge-based SLAM system","authors":"Fabian Schenk, F. Fraundorfer","doi":"10.1109/ICRA.2019.8794462","DOIUrl":"https://doi.org/10.1109/ICRA.2019.8794462","url":null,"abstract":"Simultaneous Localization and Mapping is a key requirement for many practical applications in robotics. In this work, we present RESLAM, a novel edge-based SLAM system for RGBD sensors. Due to their sparse representation, larger convergence basin and stability under illumination changes, edges are a promising alternative to feature-based or other direct approaches. We build a complete SLAM pipeline with camera pose estimation, sliding window optimization, loop closure and relocalisation capabilities that utilizes edges throughout all steps. In our system, we additionally refine the initial depth from the sensor, the camera poses and the camera intrinsics in a sliding window to increase accuracy. Further, we introduce an edge-based verification for loop closures that can also be applied for relocalisation. We evaluate RESLAM on wide variety of benchmark datasets that include difficult scenes and camera motions and also present qualitative results. We show that this novel edge-based SLAM system performs comparable to state-of-the-art methods, while running in real-time on a CPU. RESLAM is available as open-source software1.1Code is available: https://github.com/fabianschenk/RESLAM","PeriodicalId":6730,"journal":{"name":"2019 International Conference on Robotics and Automation (ICRA)","volume":"170 1","pages":"154-160"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74872731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-20DOI: 10.1109/ICRA.2019.8794410
M. Abou-Hussein, Stefan H. Müller-Weinfurtner, J. Boedecker
We study the end-to-end steering problem using visual input data from an onboard vehicle camera. An empirical comparison between spatial, spatio-temporal and multimodal models is performed assessing each concept’s performance from two points of evaluation. First, how close the model is in predicting and imitating a real-life driver’s behavior, second, the smoothness of the predicted steering command. The latter is a newly proposed metric. Building on our results, we propose a new recurrent multimodal model. The suggested model has been tested on a custom dataset recorded by BMW, as well as the public dataset provided by Udacity. Results show that it outperforms previously released scores. Further, a steering correction concept from off-lane driving through the inclusion of correction frames is presented. We show that our suggestion leads to promising results empirically.
{"title":"Multimodal Spatio-Temporal Information in End-to-End Networks for Automotive Steering Prediction","authors":"M. Abou-Hussein, Stefan H. Müller-Weinfurtner, J. Boedecker","doi":"10.1109/ICRA.2019.8794410","DOIUrl":"https://doi.org/10.1109/ICRA.2019.8794410","url":null,"abstract":"We study the end-to-end steering problem using visual input data from an onboard vehicle camera. An empirical comparison between spatial, spatio-temporal and multimodal models is performed assessing each concept’s performance from two points of evaluation. First, how close the model is in predicting and imitating a real-life driver’s behavior, second, the smoothness of the predicted steering command. The latter is a newly proposed metric. Building on our results, we propose a new recurrent multimodal model. The suggested model has been tested on a custom dataset recorded by BMW, as well as the public dataset provided by Udacity. Results show that it outperforms previously released scores. Further, a steering correction concept from off-lane driving through the inclusion of correction frames is presented. We show that our suggestion leads to promising results empirically.","PeriodicalId":6730,"journal":{"name":"2019 International Conference on Robotics and Automation (ICRA)","volume":"16 1","pages":"8641-8647"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79250427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-20DOI: 10.1109/ICRA.2019.8793705
Yebin Wang
With node selection being directed by a heuristic cost [1]–[3], A-search guided tree (AGT) is constructed on-the-fly and enables fast kinodynamic planning. This work presents two variants of AGT to improve computation efficiency. An improved AGT (i-AGT) biases node expansion through prioritizing control actions, an analogy of prioritizing nodes. Focusing on node selection, a bi-directional AGT (BAGT) introduces a second tree originated from the goal in order to offer a better heuristic cost of the first tree. Effectiveness of BAGT pivots on the fact that the second tree encodes obstacles information near the goal. Case study demonstrates that i-AGT consistently reduces the complexity of the tree and improves computation efficiency; and BAGT works largely but not always, particularly with no benefit observed for simple cases.
通过启发式代价(heuristic cost)[1] -[3]指导节点选择，实时构建a -search guided tree (AGT)，实现快速的动力学规划。为了提高计算效率，本文提出了AGT的两种变体。一种改进的AGT (i-AGT)通过对控制动作进行优先级排序来影响节点的扩展，类似于对节点进行优先级排序。双向AGT (BAGT)以节点选择为重点，引入了从目标出发的第二棵树，以提供第一棵树的更好的启发式成本。BAGT的有效性取决于第二棵树对目标附近的障碍物信息进行编码。实例研究表明，i-AGT持续降低了树的复杂度，提高了计算效率;BAGT在很大程度上起作用，但并非总是如此，特别是在简单的情况下没有观察到任何好处。
{"title":"Improved A-search guided tree construction for kinodynamic planning","authors":"Yebin Wang","doi":"10.1109/ICRA.2019.8793705","DOIUrl":"https://doi.org/10.1109/ICRA.2019.8793705","url":null,"abstract":"With node selection being directed by a heuristic cost [1]–[3], A-search guided tree (AGT) is constructed on-the-fly and enables fast kinodynamic planning. This work presents two variants of AGT to improve computation efficiency. An improved AGT (i-AGT) biases node expansion through prioritizing control actions, an analogy of prioritizing nodes. Focusing on node selection, a bi-directional AGT (BAGT) introduces a second tree originated from the goal in order to offer a better heuristic cost of the first tree. Effectiveness of BAGT pivots on the fact that the second tree encodes obstacles information near the goal. Case study demonstrates that i-AGT consistently reduces the complexity of the tree and improves computation efficiency; and BAGT works largely but not always, particularly with no benefit observed for simple cases.","PeriodicalId":6730,"journal":{"name":"2019 International Conference on Robotics and Automation (ICRA)","volume":"1 1","pages":"5530-5536"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79853947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-20DOI: 10.1109/ICRA.2019.8793492
Jian Dou, Jianru Xue, Jianwu Fang
This paper proposes a SEG-VoxelNet that takes RGB images and LiDAR point clouds as inputs for accurately detecting 3D vehicles in autonomous driving scenarios, which for the first time introduces semantic segmentation technique to assist the 3D LiDAR point cloud based detection. Specifically, SEG-VoxelNet is composed of two sub-networks: an image semantic segmentation network (SEG-Net) and an improved-VoxelNet. The SEG-Net generates the semantic segmentation map which represents the probability of the category for each pixel. The improved-VoxelNet is capable of effectively fusing point cloud data with image semantic feature and generating accurate 3D bounding boxes of vehicles. Experiments on the KITTI 3D vehicle detection benchmark show that our approach outperforms the methods of state-of-the-art.
{"title":"SEG-VoxelNet for 3D Vehicle Detection from RGB and LiDAR Data","authors":"Jian Dou, Jianru Xue, Jianwu Fang","doi":"10.1109/ICRA.2019.8793492","DOIUrl":"https://doi.org/10.1109/ICRA.2019.8793492","url":null,"abstract":"This paper proposes a SEG-VoxelNet that takes RGB images and LiDAR point clouds as inputs for accurately detecting 3D vehicles in autonomous driving scenarios, which for the first time introduces semantic segmentation technique to assist the 3D LiDAR point cloud based detection. Specifically, SEG-VoxelNet is composed of two sub-networks: an image semantic segmentation network (SEG-Net) and an improved-VoxelNet. The SEG-Net generates the semantic segmentation map which represents the probability of the category for each pixel. The improved-VoxelNet is capable of effectively fusing point cloud data with image semantic feature and generating accurate 3D bounding boxes of vehicles. Experiments on the KITTI 3D vehicle detection benchmark show that our approach outperforms the methods of state-of-the-art.","PeriodicalId":6730,"journal":{"name":"2019 International Conference on Robotics and Automation (ICRA)","volume":"69 1","pages":"4362-4368"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83335920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-20DOI: 10.1109/ICRA.2019.8793686
Chuanqi Zheng, Kiju Lee
This paper presents a new passive wheel-leg transformation mechanism and its embodiment in a small mobile robot. The mechanism is based on a unique geared structure, allowing the wheel to transform between two modes, i.e., wheel or leg, potentially adapting to varying ground conditions. It consists of a central gear and legs with partial gears that rotate around the central gear to open or close the legs. When fully closed, the mechanism forms a seamless circular wheel; when opened, it operates in the leg mode. The central gear actuated by the driving motor generates opening and closing motions of the legs without using an additional actuator. The number of legs, their physical size, and the gear ratio between the central gear and the partial gears on the legs are adjustable. This design is mechanically simple, customizable, and easy to fabricate. For physical demonstration and experiments, a mobile robotic platform was built and its terrainability was tested using five different sets of the transformable wheels with varying sizes and gear ratios. For each design, the performance with successful wheel-leg transformation, obstacle climbing, and locomotion capabilities was tested in different ground conditions.
{"title":"WheeLeR: Wheel-Leg Reconfigurable Mechanism with Passive Gears for Mobile Robot Applications","authors":"Chuanqi Zheng, Kiju Lee","doi":"10.1109/ICRA.2019.8793686","DOIUrl":"https://doi.org/10.1109/ICRA.2019.8793686","url":null,"abstract":"This paper presents a new passive wheel-leg transformation mechanism and its embodiment in a small mobile robot. The mechanism is based on a unique geared structure, allowing the wheel to transform between two modes, i.e., wheel or leg, potentially adapting to varying ground conditions. It consists of a central gear and legs with partial gears that rotate around the central gear to open or close the legs. When fully closed, the mechanism forms a seamless circular wheel; when opened, it operates in the leg mode. The central gear actuated by the driving motor generates opening and closing motions of the legs without using an additional actuator. The number of legs, their physical size, and the gear ratio between the central gear and the partial gears on the legs are adjustable. This design is mechanically simple, customizable, and easy to fabricate. For physical demonstration and experiments, a mobile robotic platform was built and its terrainability was tested using five different sets of the transformable wheels with varying sizes and gear ratios. For each design, the performance with successful wheel-leg transformation, obstacle climbing, and locomotion capabilities was tested in different ground conditions.","PeriodicalId":6730,"journal":{"name":"2019 International Conference on Robotics and Automation (ICRA)","volume":"23 1","pages":"9292-9298"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87880869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-20DOI: 10.1109/ICRA.2019.8793477
Riku Funada, María Santos, J. Yamauchi, T. Hatanaka, M. Fujita, M. Egerstedt
This paper presents a coverage control strategy for teams of quadcopters that ensures that no area is left unsurveyed in between the fields of view of the visual sensors mounted on the quadcopters. We present a locational cost that quantifies the team’s coverage performance according to the sensors’ performance function. Moreover, the cost function penalizes overlaps between the fields of view of the different sensors, with the objective of increasing the area covered by the team. A distributed control law is derived for the quadcopters so that they adjust their position and zoom according to the direction of ascent of the cost. Control barrier functions are implemented to ensure that, while executing the gradient ascent control law, no holes appear in between the fields of view of neighboring robots. The performance of the algorithm is evaluated in simulated experiments.
{"title":"Visual Coverage Control for Teams of Quadcopters via Control Barrier Functions","authors":"Riku Funada, María Santos, J. Yamauchi, T. Hatanaka, M. Fujita, M. Egerstedt","doi":"10.1109/ICRA.2019.8793477","DOIUrl":"https://doi.org/10.1109/ICRA.2019.8793477","url":null,"abstract":"This paper presents a coverage control strategy for teams of quadcopters that ensures that no area is left unsurveyed in between the fields of view of the visual sensors mounted on the quadcopters. We present a locational cost that quantifies the team’s coverage performance according to the sensors’ performance function. Moreover, the cost function penalizes overlaps between the fields of view of the different sensors, with the objective of increasing the area covered by the team. A distributed control law is derived for the quadcopters so that they adjust their position and zoom according to the direction of ascent of the cost. Control barrier functions are implemented to ensure that, while executing the gradient ascent control law, no holes appear in between the fields of view of neighboring robots. The performance of the algorithm is evaluated in simulated experiments.","PeriodicalId":6730,"journal":{"name":"2019 International Conference on Robotics and Automation (ICRA)","volume":"4 1","pages":"3010-3016"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87975156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-20DOI: 10.1109/ICRA.2019.8794281
A. D. Oliveira, Kevin Warburton, J. Sulzer, A. Deshpande
Robotic exoskeletons open up promising interventions during post-stroke rehabilitation by assisting individuals with sensorimotor impairments to complete therapy tasks. These devices have the ability to provide variable assistance tailored to individual-specific needs and, additionally, can measure several parameters associated with the movement execution. Metrics representative of movement quality are important to guide individualized treatment. While robots can provide data with high resolution, robustness, and consistency, the delineation of the human contribution in the presence of the kinematic guidance introduced by the robotic assistance is a significant challenge. In this paper, we propose a method for assessing voluntary effort from an individual fitted in an upper-body exoskeleton called Harmony. The method separates the active torques generated by the wearer from the effects caused by unmodeled dynamics and passive neuromuscular properties and involuntary forces. Preliminary results show that the effort estimated using the proposed method is consistent with the effort associated with muscle activity and is also sensitive to different levels, indicating that it can reliably evaluate user’s contribution to movement. This method has the potential to serve as a high resolution assessment tool to monitor progress of movement quality throughout the treatment and evaluate motor recovery.
{"title":"Effort Estimation in Robot-aided Training with a Neural Network","authors":"A. D. Oliveira, Kevin Warburton, J. Sulzer, A. Deshpande","doi":"10.1109/ICRA.2019.8794281","DOIUrl":"https://doi.org/10.1109/ICRA.2019.8794281","url":null,"abstract":"Robotic exoskeletons open up promising interventions during post-stroke rehabilitation by assisting individuals with sensorimotor impairments to complete therapy tasks. These devices have the ability to provide variable assistance tailored to individual-specific needs and, additionally, can measure several parameters associated with the movement execution. Metrics representative of movement quality are important to guide individualized treatment. While robots can provide data with high resolution, robustness, and consistency, the delineation of the human contribution in the presence of the kinematic guidance introduced by the robotic assistance is a significant challenge. In this paper, we propose a method for assessing voluntary effort from an individual fitted in an upper-body exoskeleton called Harmony. The method separates the active torques generated by the wearer from the effects caused by unmodeled dynamics and passive neuromuscular properties and involuntary forces. Preliminary results show that the effort estimated using the proposed method is consistent with the effort associated with muscle activity and is also sensitive to different levels, indicating that it can reliably evaluate user’s contribution to movement. This method has the potential to serve as a high resolution assessment tool to monitor progress of movement quality throughout the treatment and evaluate motor recovery.","PeriodicalId":6730,"journal":{"name":"2019 International Conference on Robotics and Automation (ICRA)","volume":"175 1","pages":"563-569"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85838198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-20DOI: 10.1109/ICRA.2019.8793827
Tung D. Ta, T. Umedachi, Y. Kawahara
Soft-bodied robots are getting attention from researchers as their potential in designing compliant and adaptive robots. However, soft-bodied robots also pose many challenges not only in non-linear controlling but also in design and fabrication. Especially, the non-compatibility between soft materials and rigid sensors/actuators makes it more difficult to design a fully compliant soft-bodied robot. In this paper, we propose an all-printed sensor and actuator for designing softbodied robots by printing silver nano-particle ink on top of a flexible plastic film. We can print bending sensors and thermal based actuators instantly with home-commodity inkjet printers without any pre/post-processing. We exemplify the application of this fabrication method with an all-printed paper caterpillar robots which can inch forward and sense its body bending angle.
{"title":"Inkjet Printable Actuators and Sensors for Soft-bodied Crawling Robots","authors":"Tung D. Ta, T. Umedachi, Y. Kawahara","doi":"10.1109/ICRA.2019.8793827","DOIUrl":"https://doi.org/10.1109/ICRA.2019.8793827","url":null,"abstract":"Soft-bodied robots are getting attention from researchers as their potential in designing compliant and adaptive robots. However, soft-bodied robots also pose many challenges not only in non-linear controlling but also in design and fabrication. Especially, the non-compatibility between soft materials and rigid sensors/actuators makes it more difficult to design a fully compliant soft-bodied robot. In this paper, we propose an all-printed sensor and actuator for designing softbodied robots by printing silver nano-particle ink on top of a flexible plastic film. We can print bending sensors and thermal based actuators instantly with home-commodity inkjet printers without any pre/post-processing. We exemplify the application of this fabrication method with an all-printed paper caterpillar robots which can inch forward and sense its body bending angle.","PeriodicalId":6730,"journal":{"name":"2019 International Conference on Robotics and Automation (ICRA)","volume":"98 1","pages":"3658-3664"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76141855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-20DOI: 10.1109/ICRA.2019.8794302
Jun Sheng, N. Deaton, J. Desai
This paper presents the development of a fiber Bragg grating (FBG) bending sensor for shape memory alloy (SMA) bending modules. Due to the small form factor, low cost, and large-deflection capability, SMA bending modules can be used to construct disposable surgical robots for a variety of minimally invasive procedures. To realize a closed-loop control of SMA bending modules, an intrinsic bending sensor is imperative. Due to the lack of bending sensors for SMA bending modules, we have developed an FBG bending sensor by integrating FBG fibers with a superelastic substrate using flexible adhesive. Since the substrate is ultra-thin and adhesive is flexible, the sensor has low stiffness and can measure large curvatures. Additionally, due to the orthogonal arrangement of the sensor/actuator assembly, the influence of temperature variation caused by SMA actuation can be compensated. The working principle of the developed sensor was modeled followed by simulations. After experimentally evaluating the developed model, the sensor was integrated with an SMA bending module and cyclically bi-directionally deflected. The experimental results proved the relatively high measurement accuracy, high repeatability, and large measurable curvatures of the sensor, although hysteresis was observed due to friction.
{"title":"A Large-Deflection FBG Bending Sensor for SMA Bending Modules for Steerable Surgical Robots","authors":"Jun Sheng, N. Deaton, J. Desai","doi":"10.1109/ICRA.2019.8794302","DOIUrl":"https://doi.org/10.1109/ICRA.2019.8794302","url":null,"abstract":"This paper presents the development of a fiber Bragg grating (FBG) bending sensor for shape memory alloy (SMA) bending modules. Due to the small form factor, low cost, and large-deflection capability, SMA bending modules can be used to construct disposable surgical robots for a variety of minimally invasive procedures. To realize a closed-loop control of SMA bending modules, an intrinsic bending sensor is imperative. Due to the lack of bending sensors for SMA bending modules, we have developed an FBG bending sensor by integrating FBG fibers with a superelastic substrate using flexible adhesive. Since the substrate is ultra-thin and adhesive is flexible, the sensor has low stiffness and can measure large curvatures. Additionally, due to the orthogonal arrangement of the sensor/actuator assembly, the influence of temperature variation caused by SMA actuation can be compensated. The working principle of the developed sensor was modeled followed by simulations. After experimentally evaluating the developed model, the sensor was integrated with an SMA bending module and cyclically bi-directionally deflected. The experimental results proved the relatively high measurement accuracy, high repeatability, and large measurable curvatures of the sensor, although hysteresis was observed due to friction.","PeriodicalId":6730,"journal":{"name":"2019 International Conference on Robotics and Automation (ICRA)","volume":"16 1","pages":"900-906"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88258441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-20DOI: 10.1109/ICRA.2019.8793709
Hirokatsu Kataoka, Y. Satoh
The paper presents an unsupervised out-of-context action (O2CA) paradigm that is based on facilitating understanding by separately presenting both human action and context within a video sequence. As a means of generating an unsupervised label, we comprehensively evaluate responses from action-based (ActionNet) and context-based (ContextNet) convolutional neural networks (CNNs). Additionally, we have created three synthetic databases based on the human action (UCF101, HMDB51) and motion capture (mocap) (SURREAL) datasets. We then conducted experimental comparisons between our approach and conventional approaches. We also compared our unsupervised learning method with supervised learning using an O2CA ground truth given by synthetic data. From the results obtained, we achieved a 96.8 score on Synth-UCF, a 96.8 score on Synth-HMDB, and 89.0 on SURREAL-O2CA with F-score.
{"title":"Unsupervised Out-of-context Action Understanding","authors":"Hirokatsu Kataoka, Y. Satoh","doi":"10.1109/ICRA.2019.8793709","DOIUrl":"https://doi.org/10.1109/ICRA.2019.8793709","url":null,"abstract":"The paper presents an unsupervised out-of-context action (O2CA) paradigm that is based on facilitating understanding by separately presenting both human action and context within a video sequence. As a means of generating an unsupervised label, we comprehensively evaluate responses from action-based (ActionNet) and context-based (ContextNet) convolutional neural networks (CNNs). Additionally, we have created three synthetic databases based on the human action (UCF101, HMDB51) and motion capture (mocap) (SURREAL) datasets. We then conducted experimental comparisons between our approach and conventional approaches. We also compared our unsupervised learning method with supervised learning using an O2CA ground truth given by synthetic data. From the results obtained, we achieved a 96.8 score on Synth-UCF, a 96.8 score on Synth-HMDB, and 89.0 on SURREAL-O2CA with F-score.","PeriodicalId":6730,"journal":{"name":"2019 International Conference on Robotics and Automation (ICRA)","volume":"11 1","pages":"8227-8233"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88391299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：