A large number of studies analyse object detection and pose estimation at visual level in 2D , discussing the effects of challenges such as occlusion , clutter , texture , etc . , on the performances of the methods , which work in the context of RGB modality . Interpreting the depth data , the study in this paper presents thorough multi-modal analyses . The adapted model is evaluated on the FLIC dataset . Our network out-performed the default MobileNets for pose estimation , as well as achieved performance comparable to the state of the art results while reducing inference time significantly . The key concepts proposed are representations of spatial orienta­ tion that result in continuous distance measures , and the choice of a fixed network topology that is best suited to the representation of 3-D orientation . Experimen­ tal results from both simulated and real images demonstrate that a pose estimate within the accuracy requirements can be found in more than 90 % of all cases . The current implementation operates at near frame rate on real world images . To complete the estimation , a handheld equipment height model , with respect to the Digital Terrain Model contained in Geographical Information Systems , is used for the displacement on the vertical axis . In addition , an accurate pose estimation based on the recognition of known objects is punctually used to correct the pose estimate and reset the monocular Visual Odometry . To validate the benefit of our framework , experimental data have been collected on a 0.7 km pedestrian path in an urban environment for various people . We predict expected feature quantity by combing detection probability ( statistical model ) of each feature and idle feature quantity ( object model ) of an object that we can see current viewpoint . Moreover , we calculate each feature 's reliability by combining utility function to decide if expected features are valid in object recognition and pose estimation process . Based on the final probability , we decide the optimal feature . ” A baseline approach would be to first perform pose estimation and then detect the touch codes based on the estimated joint locations . We found that this sequential approach does not perform well because pose estimation step is too unreliable for images of interacting people , due to difficulties with occlusion and limb ambiguities . Instead , we propose a direct approach where we build an articulated model tuned for each touch code . ←gi∖gi{p * } Update c by averaging the person centroid hypotheses : ←c∑∈ ( pv , n ) P / ⁢fn ( pv ) |P| Update ←R∪R{P } subsection : Local Greedy Inference for Pose Estimation According to Eqn . In this work , we present a trivial yet effective solution to generate 3D animation of multiple persons from a 2D video using deep learning . Although significant improvement has been achieved recently in 3D human pose estimation , most of the prior works work well in case of single person pose estimation and multi-person pose estimation is still a challenging problem . In this work , we firstly propose a supervised multi-person 3D pose estimation and animation framework namely AnimePose for a given input RGB video sequence . This technique can be easily integrated into any state-of-the-art network architecture that produces pixel-wise predictions . We show how to apply this method to multi-person pose estimation and report state-of-the-art performance on the MPII and MS-COCO datasets . Abstract Human motion recognition based on computer vision plays an important role in many fields , such as video surveillance , virtual reality , and medical care . To solve the inaccurate multi-person pose estimation problem and improve the generalizability of the extracted features , this paper proposes a multi-person pose estimation method based on a deep convolutional neural network . This method mainly relies on a top-down structure which includes two stages . The task of multi-person human pose estimation in natural scenes is quite challenging . Existing methods include both top-down and bottom-up approaches . These two regression tasks are naturally dependent on each other . In this work , we propose a dual-path network specially designed for multi-person human pose estimation , and compare our performance with the openpose network in aspects of model size , forward speed , and estimation accuracy . In this paper , we present a method for real-time multi-person human pose estimation from video by utilizing convolutional neural networks . Our method is aimed for use case specific applications , where good accuracy is essential and variation of the background and poses is limited . We present an efficient multi-person pose-tracking method , KeyTrack that only relies on keypoint information without using any RGB or optical flow to locate and track human keypoints in real-time . KeyTrack is a top-down approach that learns spatio-temporal pose relationships by modeling the multi-person pose-tracking problem as a novel Pose Entailment task using a Transformer based architecture . Furthermore , KeyTrack uses a novel , parameter-free , keypoint refinement technique that improves the keypoint estimates used by the Transformers . The OpenPose is a kind of a deep learning based pose estimator which achieved a top accuracy for multiple person pose estimations . Even if using the OpenPose , it is necessary to used high-performance GPU since it requires massive parameters access with high-bandwidth off-chip GDDR5 memories and a higher operation clock frequency . Due to the trade off between model complexity and estimation accuracy , current human pose estimators either are of low accuracy or requires long running time . Such dilemma is especially severe in real time multi-person pose estimation . To address this issue , we design a deep network of reduced parameter size and high estimation accuracy , via introducing deconvolution layers instead of widely used fully-connected configuration . The proposed Parsing Induced Learner is composed of a parsing encoder and a pose model parameter adapter , which together learn to predict dynamic parameters of the pose model to extract complementary useful features for more accurate pose estimation . Comprehensive experiments on benchmarks LIP and extended PASCAL-Person-Part show that the proposed Parsing Induced Learner can improve performance of both single- and multi-person pose estimation to new state-of-the-art . Cross-dataset experiments also show that the proposed Parsing Induced Learner from LIP dataset can accelerate learning of a human pose estimation model on MPII benchmark in addition to achieving outperforming performance . Our formulation , an instance of an integer linear program , implicitly performs non-maximum suppression on the set of part candidates and groups them to form configurations of body parts respecting geometric and appearance constraints . Experiments on four different datasets demonstrate state-of-the-art results for both single person and multi person pose estimation . document : MultiPoseNet : Fast Multi - Person Pose Estimation using Pose Residual Network In this paper , we present MultiPoseNet , Hourglass blocks , ( HG ) developed by Newell et al . , are basically convolution - deconvolution structures with residual connections . Newell et al . stacked HG blocks to obtain an iterative refinement process and showed its effectiveness on single person pose estimation . Stacked Hourglass ( SHG ) subsubsection : Bottom - up Multi person pose estimation solutions branched out as bottom - up and top - down methods . Bottom - up approaches detect body joints and assign them to people instances , therefore they are faster in test time and smaller in size compared to top - down approaches . document : Generative Partition Networks for Multi - Person Pose Estimation This paper proposes a new Generative Partition Network ( GPN ) to address the challenging multi - person pose estimation problem . paragraph : Top - Down Multi - Person Pose Estimation Existing approaches following top - down strategy sequentially perform person detection and single - person pose estimation . In , Gkioxari et al . proposed to adopt the Generalized Hough Transform framework to first generate person proposals and then classify joint candidates based on the poselets . Figure [ reference ] visualizes some pose estimation results on these three datasets . We can observe that the proposed GPN model estimates multi - person poses accurately and robustly even in challenging scenarios , , joint occlusion caused by a person of interest and other overlapped persons presenting in the first example of MPII dataset , large pose variation shown in the second example of the extended PASCAL - Person - Part dataset , and appearance and illumination changes in the forth example of WAF dataset . These results also verify the effectiveness of GPN for producing reliable joint detections and partitions in multi - person pose estimation . Then , we demonstrate superior performance of on the multi person pose estimation task . subsection : Single Person Pose Estimation We now evaluate single person ( SP ) and more general multi person ( MP ) DeepCut models on LSP and MPII SP benchmarks described in Sec . Second , CornerNet uses corner pooling to better localize the corners . Our approach is inspired by on Associative Embedding in the context of multi - person pose estimation . Newell et al . propose an approach that detects and groups human joints in a single network . This enables augmented reality and robotic applications and leaves room for tracking algorithms . Multiple encoders and corresponding codebooks fit into the GPU memory , making multi - object pose estimation feasible . width=0.8 width= To that end we rely on a graph partitioning approach closely related to . In contrast to who focus on pedestrian tracking , and who perform single frame multi - person pose estimation , we solve a more complex problem of articulated multi - person pose tracking . Earlier approaches to articulated pose tracking in monocular videos rely on hand - crafted image representations and focus on simplified tasks , such as tracking upper body poses of frontal isolated people , or tracking walking pedestrians with little degree of articulation . In contrast , we address a harder problem of multi - person articulated pose tracking and do not make assumptions about the type of body motions or activities of people . Our approach is closely related to who propose a similar formulation based on graph partitioning . As mentioned above , this difference in accuracy indicates the merit of having at our disposal DensePose - COCO for discriminative training . subsection : Multi - Person Dense Pose Estimation Having established the merit of the DensePose - COCO dataset , we now turn to examining the impact of network architecture on dense pose estimation in - the - wild . The framework includes a procedure to compute the Grasp Matrix and the Hand Jacobian for objects and fingertips of arbitrary shape considering rolling contacts without slipping . The usefulness of the proposed approach is illustrated in a case study of a pair of underactuated fingers driven by a tendon-pulley differential transmission mechanism and capable of performing in-hand object manipulation . The manipulability region for different object and fingertip shapes is computed and the results are discussed . Egocentric cameras can be used to benefit such tasks as analyzing fine motor skills , recognizing gestures and learning about hand-object manipulation . To enable such technology , we believe that the hands must detected on the pixel-level to gain important information about the shape of the hands and fingers . Specifically , we focus on recognizing hand grasp types , object attributes and manipulation actions within an unified framework by exploring their contextual relationships . Our hypothesis is that it is necessary to jointly model hands , objects and actions in order to accurately recognize multiple tasks that are correlated to each other in hand-object manipulation . In the proposed model , we explore various semantic relationships between actions , grasp types and object attributes , and show how the context can be used to boost the recognition of each component . As a result , this study aims to decode the different hand gestures from sEMG signal . The thumb plays the most important role in hand-based object manipulation , such as touch screen control for smart phones , for which many thumb-based hand involved . Therefore , studying the relationship between EMG signals and the thumb movement has certain value for the future human-computer interaction . We analyze time-travel films and then define three design concepts of a history tool . Second , we ideate possible interfaces for a history tool considering hand-based object manipulation . We present the interface types using hands , other body parts , and an additional device . The model of contact mechanics supports the analysis of contact situations with multiple sliding contacts , allowing it to solve problems not solvable if only rolling contacts are allowed . Based on the proposed methodology , a planner would effectively solve two-point boundary value problems by using contact formation transitions to discretize the configuration space of , for example , a hand/object system . Within each discrete cell , or contact formation a model of contact mechanics is used to generate trajectories joining the cells and building a contact formation tree . This thesis focuses on inference of the full degree-of-freedom hand pose and semantic hand gestures from the visual image inputs . Particularly , the RGB-D camera is selected as the input device to record the hand images for the proposed methods in this thesis , since it can capture more detailed 3D structure of the hand and is thus less ambiguous for hand pose and gesture inference . To develop a practical solution , the various related aspects have been investigated , such as hand part recognition , fingertip tracking and hand skeleton extraction . This proposal utilizes a specially designed gripper tip mounted on the jaws of a two-fingered gripper . With the modified gripper , in-hand manipulation of the grasped object is realized , which can ensure the control of the orientation and offset position of the grasped object . The proposal in this paper is verified by a simulated assembly in which a robot arm completed the assembly process including parts picking from bin and a subsequent peg-in-hole assembly . We validate this model physically using a hardware set-up . We then demonstrate a simple quasi-static Jacobian controller which solves a second-order cone program to achieve the task of in-hand object repositioning . Thesis Supervisor : Prediction of contact forces is a central issue in robotic grasping analysis and the indetermination of the contact force is one of the most important characteristic for the object-hand system . In this paper , we provide a unified framework to determine the contact force in robotic grasping . A control scheme to ensure firm grip , avoid slippage and well track a given motion imposed to the object is designed . To verify the reasonableness of the design and the control scheme , we simulate the object manipulation of the dexterous hand using Adams . A comparison is made between the calculated results carried out by Matlab and the operation results of the virtual simulation model established by Adams .
A large number of studies analyse object detection and pose estimation at visual level in 2D , discussing the effects of challenges such as occlusion , clutter , texture , etc . , on the performances of the methods , which work in the context of RGB modality . Interpreting the depth data , the study in this paper presents thorough multi-modal analyses .
The adapted model is evaluated on the FLIC dataset . Our network out-performed the default MobileNets for pose estimation , as well as achieved performance comparable to the state of the art results while reducing inference time significantly .
The key concepts proposed are representations of spatial orienta­ tion that result in continuous distance measures , and the choice of a fixed network topology that is best suited to the representation of 3-D orientation . Experimen­ tal results from both simulated and real images demonstrate that a pose estimate within the accuracy requirements can be found in more than 90 % of all cases . The current implementation operates at near frame rate on real world images .
To complete the estimation , a handheld equipment height model , with respect to the Digital Terrain Model contained in Geographical Information Systems , is used for the displacement on the vertical axis . In addition , an accurate pose estimation based on the recognition of known objects is punctually used to correct the pose estimate and reset the monocular Visual Odometry . To validate the benefit of our framework , experimental data have been collected on a 0.7 km pedestrian path in an urban environment for various people .
We predict expected feature quantity by combing detection probability ( statistical model ) of each feature and idle feature quantity ( object model ) of an object that we can see current viewpoint . Moreover , we calculate each feature 's reliability by combining utility function to decide if expected features are valid in object recognition and pose estimation process . Based on the final probability , we decide the optimal feature .
” A baseline approach would be to first perform pose estimation and then detect the touch codes based on the estimated joint locations . We found that this sequential approach does not perform well because pose estimation step is too unreliable for images of interacting people , due to difficulties with occlusion and limb ambiguities . Instead , we propose a direct approach where we build an articulated model tuned for each touch code .
←gi∖gi{p * } Update c by averaging the person centroid hypotheses : ←c∑∈ ( pv , n ) P / ⁢fn ( pv ) |P| Update ←R∪R{P } subsection : Local Greedy Inference for Pose Estimation According to Eqn .
In this work , we present a trivial yet effective solution to generate 3D animation of multiple persons from a 2D video using deep learning . Although significant improvement has been achieved recently in 3D human pose estimation , most of the prior works work well in case of single person pose estimation and multi-person pose estimation is still a challenging problem . In this work , we firstly propose a supervised multi-person 3D pose estimation and animation framework namely AnimePose for a given input RGB video sequence .
This technique can be easily integrated into any state-of-the-art network architecture that produces pixel-wise predictions . We show how to apply this method to multi-person pose estimation and report state-of-the-art performance on the MPII and MS-COCO datasets .
Abstract Human motion recognition based on computer vision plays an important role in many fields , such as video surveillance , virtual reality , and medical care . To solve the inaccurate multi-person pose estimation problem and improve the generalizability of the extracted features , this paper proposes a multi-person pose estimation method based on a deep convolutional neural network . This method mainly relies on a top-down structure which includes two stages .
The task of multi-person human pose estimation in natural scenes is quite challenging . Existing methods include both top-down and bottom-up approaches .
These two regression tasks are naturally dependent on each other . In this work , we propose a dual-path network specially designed for multi-person human pose estimation , and compare our performance with the openpose network in aspects of model size , forward speed , and estimation accuracy .
In this paper , we present a method for real-time multi-person human pose estimation from video by utilizing convolutional neural networks . Our method is aimed for use case specific applications , where good accuracy is essential and variation of the background and poses is limited .
We present an efficient multi-person pose-tracking method , KeyTrack that only relies on keypoint information without using any RGB or optical flow to locate and track human keypoints in real-time . KeyTrack is a top-down approach that learns spatio-temporal pose relationships by modeling the multi-person pose-tracking problem as a novel Pose Entailment task using a Transformer based architecture . Furthermore , KeyTrack uses a novel , parameter-free , keypoint refinement technique that improves the keypoint estimates used by the Transformers .
The OpenPose is a kind of a deep learning based pose estimator which achieved a top accuracy for multiple person pose estimations . Even if using the OpenPose , it is necessary to used high-performance GPU since it requires massive parameters access with high-bandwidth off-chip GDDR5 memories and a higher operation clock frequency .
Due to the trade off between model complexity and estimation accuracy , current human pose estimators either are of low accuracy or requires long running time . Such dilemma is especially severe in real time multi-person pose estimation . To address this issue , we design a deep network of reduced parameter size and high estimation accuracy , via introducing deconvolution layers instead of widely used fully-connected configuration .
The proposed Parsing Induced Learner is composed of a parsing encoder and a pose model parameter adapter , which together learn to predict dynamic parameters of the pose model to extract complementary useful features for more accurate pose estimation . Comprehensive experiments on benchmarks LIP and extended PASCAL-Person-Part show that the proposed Parsing Induced Learner can improve performance of both single- and multi-person pose estimation to new state-of-the-art . Cross-dataset experiments also show that the proposed Parsing Induced Learner from LIP dataset can accelerate learning of a human pose estimation model on MPII benchmark in addition to achieving outperforming performance .
Our formulation , an instance of an integer linear program , implicitly performs non-maximum suppression on the set of part candidates and groups them to form configurations of body parts respecting geometric and appearance constraints . Experiments on four different datasets demonstrate state-of-the-art results for both single person and multi person pose estimation .
document : MultiPoseNet : Fast Multi - Person Pose Estimation using Pose Residual Network In this paper , we present MultiPoseNet ,
Hourglass blocks , ( HG ) developed by Newell et al . , are basically convolution - deconvolution structures with residual connections . Newell et al . stacked HG blocks to obtain an iterative refinement process and showed its effectiveness on single person pose estimation . Stacked Hourglass ( SHG )
subsubsection : Bottom - up Multi person pose estimation solutions branched out as bottom - up and top - down methods . Bottom - up approaches detect body joints and assign them to people instances , therefore they are faster in test time and smaller in size compared to top - down approaches .
document : Generative Partition Networks for Multi - Person Pose Estimation This paper proposes a new Generative Partition Network ( GPN ) to address the challenging multi - person pose estimation problem .
paragraph : Top - Down Multi - Person Pose Estimation Existing approaches following top - down strategy sequentially perform person detection and single - person pose estimation . In , Gkioxari et al . proposed to adopt the Generalized Hough Transform framework to first generate person proposals and then classify joint candidates based on the poselets .
Figure [ reference ] visualizes some pose estimation results on these three datasets . We can observe that the proposed GPN model estimates multi - person poses accurately and robustly even in challenging scenarios , , joint occlusion caused by a person of interest and other overlapped persons presenting in the first example of MPII dataset , large pose variation shown in the second example of the extended PASCAL - Person - Part dataset , and appearance and illumination changes in the forth example of WAF dataset . These results also verify the effectiveness of GPN for producing reliable joint detections and partitions in multi - person pose estimation .
Then , we demonstrate superior performance of on the multi person pose estimation task . subsection : Single Person Pose Estimation We now evaluate single person ( SP ) and more general multi person ( MP ) DeepCut models on LSP and MPII SP benchmarks described in Sec .
Second , CornerNet uses corner pooling to better localize the corners . Our approach is inspired by on Associative Embedding in the context of multi - person pose estimation . Newell et al . propose an approach that detects and groups human joints in a single network .
This enables augmented reality and robotic applications and leaves room for tracking algorithms . Multiple encoders and corresponding codebooks fit into the GPU memory , making multi - object pose estimation feasible . width=0.8 width=
To that end we rely on a graph partitioning approach closely related to . In contrast to who focus on pedestrian tracking , and who perform single frame multi - person pose estimation , we solve a more complex problem of articulated multi - person pose tracking . Earlier approaches to articulated pose tracking in monocular videos rely on hand - crafted image representations and focus on simplified tasks , such as tracking upper body poses of frontal isolated people , or tracking walking pedestrians with little degree of articulation . In contrast , we address a harder problem of multi - person articulated pose tracking and do not make assumptions about the type of body motions or activities of people . Our approach is closely related to who propose a similar formulation based on graph partitioning .
As mentioned above , this difference in accuracy indicates the merit of having at our disposal DensePose - COCO for discriminative training . subsection : Multi - Person Dense Pose Estimation Having established the merit of the DensePose - COCO dataset , we now turn to examining the impact of network architecture on dense pose estimation in - the - wild .
The framework includes a procedure to compute the Grasp Matrix and the Hand Jacobian for objects and fingertips of arbitrary shape considering rolling contacts without slipping . The usefulness of the proposed approach is illustrated in a case study of a pair of underactuated fingers driven by a tendon-pulley differential transmission mechanism and capable of performing in-hand object manipulation . The manipulability region for different object and fingertip shapes is computed and the results are discussed .
Egocentric cameras can be used to benefit such tasks as analyzing fine motor skills , recognizing gestures and learning about hand-object manipulation . To enable such technology , we believe that the hands must detected on the pixel-level to gain important information about the shape of the hands and fingers .
Specifically , we focus on recognizing hand grasp types , object attributes and manipulation actions within an unified framework by exploring their contextual relationships . Our hypothesis is that it is necessary to jointly model hands , objects and actions in order to accurately recognize multiple tasks that are correlated to each other in hand-object manipulation . In the proposed model , we explore various semantic relationships between actions , grasp types and object attributes , and show how the context can be used to boost the recognition of each component .
As a result , this study aims to decode the different hand gestures from sEMG signal . The thumb plays the most important role in hand-based object manipulation , such as touch screen control for smart phones , for which many thumb-based hand involved . Therefore , studying the relationship between EMG signals and the thumb movement has certain value for the future human-computer interaction .
We analyze time-travel films and then define three design concepts of a history tool . Second , we ideate possible interfaces for a history tool considering hand-based object manipulation . We present the interface types using hands , other body parts , and an additional device .
The model of contact mechanics supports the analysis of contact situations with multiple sliding contacts , allowing it to solve problems not solvable if only rolling contacts are allowed . Based on the proposed methodology , a planner would effectively solve two-point boundary value problems by using contact formation transitions to discretize the configuration space of , for example , a hand/object system . Within each discrete cell , or contact formation a model of contact mechanics is used to generate trajectories joining the cells and building a contact formation tree .
This thesis focuses on inference of the full degree-of-freedom hand pose and semantic hand gestures from the visual image inputs . Particularly , the RGB-D camera is selected as the input device to record the hand images for the proposed methods in this thesis , since it can capture more detailed 3D structure of the hand and is thus less ambiguous for hand pose and gesture inference . To develop a practical solution , the various related aspects have been investigated , such as hand part recognition , fingertip tracking and hand skeleton extraction .
This proposal utilizes a specially designed gripper tip mounted on the jaws of a two-fingered gripper . With the modified gripper , in-hand manipulation of the grasped object is realized , which can ensure the control of the orientation and offset position of the grasped object . The proposal in this paper is verified by a simulated assembly in which a robot arm completed the assembly process including parts picking from bin and a subsequent peg-in-hole assembly .
We validate this model physically using a hardware set-up . We then demonstrate a simple quasi-static Jacobian controller which solves a second-order cone program to achieve the task of in-hand object repositioning . Thesis Supervisor :
Prediction of contact forces is a central issue in robotic grasping analysis and the indetermination of the contact force is one of the most important characteristic for the object-hand system . In this paper , we provide a unified framework to determine the contact force in robotic grasping .
A control scheme to ensure firm grip , avoid slippage and well track a given motion imposed to the object is designed . To verify the reasonableness of the design and the control scheme , we simulate the object manipulation of the dexterous hand using Adams . A comparison is made between the calculated results carried out by Matlab and the operation results of the virtual simulation model established by Adams .
Recovering 6D Object Pose: A Review and Multi-modal Analysis Adapting MobileNets for mobile based upper body pose estimation A Feature Map Approach to Real-Time 3-D Object Pose Estimation from Single 2-D Perspective Views Solving Monocular Visual Odometry Scale Factor with Adaptive Step Length Estimates for Pedestrians Using Handheld Devices Statistical feature selection model for robust 3D object recognition Recognizing proxemics in personal photos Generative Partition Networks for Multi-Person Pose Estimation AnimePose: Multi-person 3D pose estimation and animation Associative Embedding: End-to-End Learning for Joint Detection and Grouping Multi-person pose estimation based on a deep convolutional neural network Dual Path Networks for Multi-Person Human Pose Estimation Real-time Human Pose Estimation from Video with Convolutional Neural Networks 15 Keypoints Is All You Need An FPGA Realization of OpenPose Based on a Sparse Weight Convolutional Neural Network A Deconvolutional Bottom-up Deep Network for multi-person pose estimation Human Pose Estimation with Parsing Induced Learner DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network Generative Partition Networks for Multi-Person Pose Estimation DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation CornerNet: Detecting Objects as Paired Keypoints Implicit 3D Orientation Learning for 6D Object Detection from RGB Images ArtTrack: Articulated Multi-Person Tracking in the Wild DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild Influence of fingertip and object shape on the manipulation ability of underactuated hands Model Recommendation with Virtual Probes for Egocentric Hand Detection Understanding hand-object manipulation by modeling the contextual relationship between actions, grasp types and object attributes Surface Emg Channel Selection For Thumb Motion Classificationsignal Designing a History Tool for a 3D Virtual Environment System A framework for planning dexterous manipulation Vision-based hand pose estimation and gesture recognition Assembly of randomly placed parts realized by using only one robot arm with a general parallel-jaw gripper Jacobian-based control of soft robots for manipulation using implicit surface models Analysis of indeterminate force in multi-contact friction grasp based on potential energy minimization Virtual prototyping and control of a dexterous hand
cfb1599e51fae50f4477535eeb574325f773fdde 8c94d77ea6e8ca9bd31b25d81d581609915d12a0 1e1e1d37d0579fd2f4a02f636e7fd7cb236c2d22 4a8e34618eafc7172f756ab554c9a5ee1c2bce11 ae13c3834aae13912f75ff5ab44fe71e9d86c33e 61be4b873672062dc61e2f757bb84fb4cc4fa360 c20fb1bdc6e0754ee4dcb7cae0c06a1d76e2cc48 239f500b6396cc97f51e5c53d165fae9ce13f661 f458913ad5d54de034537931946cf907e587d166 3884b78a06ccfde3249c16ac450b5254d033126a 7bac3fd1b0e524800b605c540922d7ece3b92724 0fdd9be2330e69c27bcecbd6371468a473bf5c35 652cd89b90ce99d1cb71dfc6c7af003c28b8e05c dbff47c5e59c8f9290bf3feeacfac75b4b2c5e42 dce84034bf4a8139ec8676fda4d5eb43cbeb6a2d c10d25ca31df02571df8958d531995e7bbf6d0b3 7c0c54d168b6e00dcb1641516a543221e0c2a025 3955eba240340ff793003dd17aedbbce9305c9bb 9c1b526dc9adba029809e45693f758c2af913f60 fca7c09caa9a3b7a0fc6df71355c765a3a064964 f527ee1932c4d47e17e3f2372875c5aa51e97f6c ef8b24ad6ae8233bf020feddaf8b7488ecbf3975 b2c46084d4cdaf407a43d3a2c213b9b1a1678206 9a91ace2b352df82cb0fc09f38614aa89323cc6b 93d45d1c58018d816a1c2d084162280d5068a1f5 84c3feb46553e18ec3943e7a265a541dcbd2af82 90ab7bca132f6caf4223d13ec7ca5899de3d19be
["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Engineering","Medicine","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Engineering","Computer Science"] ["Computer Science"] ["Computer Science","Engineering"] ["Computer Science"] ["Computer Science"] ["Computer Science"]
2017 2018 1997 2019 2011 2012 2017 2020 2017 2019 2017 2016 2019 2018 2019 2018 2015 2018 2017 2016 2018 2018 2017 2017 2017 2013 2018 2018 2019 1991 2015 2019 2017 2010 2013
AVSS DAGM-Symposium ICAR CVPR ArXiv NIPS FPT IST CVPR ECCV ArXiv 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV ECCV 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IROS ICCV ICMLC HCI (34) ICRA Humanoids
https://api.semanticscholar.org/cfb1599e51fae50f4477535eeb574325f773fdde https://api.semanticscholar.org/8c94d77ea6e8ca9bd31b25d81d581609915d12a0 https://api.semanticscholar.org/1e1e1d37d0579fd2f4a02f636e7fd7cb236c2d22 https://api.semanticscholar.org/4a8e34618eafc7172f756ab554c9a5ee1c2bce11 https://api.semanticscholar.org/ae13c3834aae13912f75ff5ab44fe71e9d86c33e https://api.semanticscholar.org/61be4b873672062dc61e2f757bb84fb4cc4fa360 https://www.semanticscholar.org/paper/b902231f1b19babdf4578be7e6f7f02ae28f1e22 https://api.semanticscholar.org/c20fb1bdc6e0754ee4dcb7cae0c06a1d76e2cc48 https://api.semanticscholar.org/239f500b6396cc97f51e5c53d165fae9ce13f661 https://api.semanticscholar.org/f458913ad5d54de034537931946cf907e587d166 https://api.semanticscholar.org/3884b78a06ccfde3249c16ac450b5254d033126a https://api.semanticscholar.org/7bac3fd1b0e524800b605c540922d7ece3b92724 https://api.semanticscholar.org/0fdd9be2330e69c27bcecbd6371468a473bf5c35 https://api.semanticscholar.org/652cd89b90ce99d1cb71dfc6c7af003c28b8e05c https://api.semanticscholar.org/dbff47c5e59c8f9290bf3feeacfac75b4b2c5e42 https://api.semanticscholar.org/dce84034bf4a8139ec8676fda4d5eb43cbeb6a2d https://api.semanticscholar.org/c10d25ca31df02571df8958d531995e7bbf6d0b3 https://www.semanticscholar.org/paper/0fbd17a4f791e04bbf8f240f7c48c178900e30a6 https://www.semanticscholar.org/paper/b902231f1b19babdf4578be7e6f7f02ae28f1e22 https://www.semanticscholar.org/paper/c10d25ca31df02571df8958d531995e7bbf6d0b3 https://www.semanticscholar.org/paper/16028acc465c434a43599f0f900c91f38d690e02 https://www.semanticscholar.org/paper/3fc26991557d28ff720edef2d284ef2663e1e530 https://www.semanticscholar.org/paper/325af39d281d5903a269c01fab8f53d7400a4c49 https://www.semanticscholar.org/paper/3d5bab25ff8e2c47a07851977635ae8c290d6e14 https://api.semanticscholar.org/7c0c54d168b6e00dcb1641516a543221e0c2a025 https://api.semanticscholar.org/3955eba240340ff793003dd17aedbbce9305c9bb https://api.semanticscholar.org/9c1b526dc9adba029809e45693f758c2af913f60 https://api.semanticscholar.org/fca7c09caa9a3b7a0fc6df71355c765a3a064964 https://api.semanticscholar.org/f527ee1932c4d47e17e3f2372875c5aa51e97f6c https://api.semanticscholar.org/ef8b24ad6ae8233bf020feddaf8b7488ecbf3975 https://api.semanticscholar.org/b2c46084d4cdaf407a43d3a2c213b9b1a1678206 https://api.semanticscholar.org/9a91ace2b352df82cb0fc09f38614aa89323cc6b https://api.semanticscholar.org/93d45d1c58018d816a1c2d084162280d5068a1f5 https://api.semanticscholar.org/84c3feb46553e18ec3943e7a265a541dcbd2af82 https://api.semanticscholar.org/90ab7bca132f6caf4223d13ec7ca5899de3d19be