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Learning from multiple demonstrations using trajectory-aware non-rigid registration with applications to deformable object manipulation
2015
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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Learning from demonstration by means of nonrigid point cloud registration is an effective tool for learning to manipulate a wide range of deformable objects. ...
In this work, we show that a trajectory-aware non-rigid registration method that uses multiple demonstrations to focus the registration process on points that are relevant to the task can effectively handle ...
LEARNING FROM MULTIPLE DEMONSTRATIONS USING TRAJECTORY-AWARE REGISTRATION While the registration method described in the previous section can be used to transfer demonstrations for a variety of deformable ...
doi:10.1109/iros.2015.7354120
dblp:conf/iros/LeeGLLA15
fatcat:2rwgq4lns5chhgh7lpivyaxnsi
Transferring Grasping Skills to Novel Instances by Latent Space Non-Rigid Registration
[article]
2018
arXiv
pre-print
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Correspondences between the instances are established by means of a non-rigid registration method that combines the Coherent Point Drift approach with subspace methods. ...
The known object instances are modeled using a canonical shape and a transformation which deforms it to match the instance shape. ...
Successful application of the non-rigid registration for transferring grasping skills was also demonstrated both with synthetic and real sensory data. ...
arXiv:1809.05353v1
fatcat:ykab5eqcrjd55mbdggx4c3srqq
Spatio-Temporal Registration of Multiple Trajectories
[chapter]
2011
Lecture Notes in Computer Science
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We demonstrate its usefulness with synthetic and real experiments. In particular, we register and analyze complex surgical gestures performed by tele-manipulation using the da Vinci robot. ...
We first define the spatio-temporal registration problem between multiple trajectories. ...
Even though we register multiple trajectories of a single object (a tool), by stacking up the data, one can extend the approach to register multiple trajectories of multiple objects. ...
doi:10.1007/978-3-642-23623-5_19
fatcat:oof6nvn7yfallknbsfi5kelpme
Perception of Deformable Objects and Compliant Manipulation for Service Robots
[chapter]
2015
Soft Robotics
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In: Soft Robotics: From Theory to Applications, A. Verl, A. Albu-Schäffer, O. Brock, A. Raatz (Eds.), Springer Vieweg, 2015. ...
In this paper, we present our approaches to compliant control and object manipulation skill transfer for service robots. We report on evaluation results and public demonstrations of our approaches. ...
Their non-rigid registration method is a variant of the thin plate spline robust point matching (TPS-RPM) algorithm. ...
doi:10.1007/978-3-662-44506-8_7
fatcat:hovm6xjncbgwxe5klh3du6cdbm
Coinbot: Intelligent Robotic Coin Bag Manipulation Using Deep Reinforcement Learning And Machine Teaching
[article]
2020
arXiv
pre-print
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However, the deformable properties of the bag along with the large quantity of rigid-body coins contained within it, significantly increases the challenges of bag detection, grasping and manipulation by ...
Leveraging a depth camera and object detection using deep learning, a bag detection and pose estimation has been done for choosing the optimal point of grasping. ...
Composite objects from MuJoCo 2.0 [19] are used to create, simulate and render complex deformable objects. ...
arXiv:2012.01356v1
fatcat:ox7q57eo7jb4bixe23l566bsca
Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review
2020
Sensors
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As there come to be more applications of intelligent robots, their task object is becoming more varied. However, it is still a challenge for a robot to handle unfamiliar objects. ...
In particular, we focus on how the robot perceives the features of an object, so as to reduce the uncertainty of objects, and how the robot completes object grasping through the learning-based approach ...
Robotic Grasping of Rigid Objects As for rigid objects, learning from demonstration (LfD) is a popular approach for a robot to improve its capability of object manipulation. ...
doi:10.3390/s20133707
pmid:32630755
pmcid:PMC7374444
fatcat:rcgkqwlgqrdv7h5mhovv5m5xdu
Author Index
2010
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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Tracking Hong, Yi Workshop: Sparse Semi-Supervised Learning for Perceptual Grouping Hontani, Hidekata Point-Based Non-Rigid Surface Registration with Accuracy Estimation Hoque, Mohammed E. ...
Histogram of Gradient Descriptors
Gu, Chunhui
Figure-Ground Segmentation Improves Handled Object Recognition in Egocentric Video
Gu, Xianfeng
Dense Non-rigid Surface Registration Using High-Order ...
doi:10.1109/cvpr.2010.5539913
fatcat:y6m5knstrzfyfin6jzusc42p54
BANMo: Building Animatable 3D Neural Models from Many Casual Videos
[article]
2021
arXiv
pre-print
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., synchronized multi-camera systems), or pre-built 3D deformable models (e.g., SMAL or SMPL). Such methods are not able to scale to diverse sets of objects in the wild. ...
When combined with canonical embeddings, such models allow us to establish dense correspondences across videos that can be self-supervised with cycle consistency. ...
to de-
non-rigid object reconstruction. ...
arXiv:2112.12761v2
fatcat:creiz2vswzdozoghhury7g5aza
ACID: Action-Conditional Implicit Visual Dynamics for Deformable Object Manipulation
[article]
2022
arXiv
pre-print
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Manipulating volumetric deformable objects in the real world, like plush toys and pizza dough, bring substantial challenges due to infinite shape variations, non-rigid motions, and partial observability ...
To accurately identify state change under large non-rigid deformations, we learn a correspondence embedding field through a novel geodesics-based contrastive loss. ...
Acknowledgement: We would like to sincerely thank Michelle Lu, Philipp Reist, and Cheng Low for help with Omniverse Kit simulation. ...
arXiv:2203.06856v2
fatcat:xnaqvxcbvbdu5fckdfp2jwn6fi
2020 Index IEEE Robotics and Automation Letters Vol. 5
2020
IEEE Robotics and Automation Letters
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., +, LRA April 2020 1127-1134 Learning Object-Action Relations from Bimanual Human Demonstration Using Graph Networks. ...
., +, LRA April 2020 1492-1499
Learning Object-Action Relations from Bimanual Human Demonstration
Using Graph Networks. ...
doi:10.1109/lra.2020.3032821
fatcat:qrnouccm7jb47ipq6w3erf3cja
Supervised Autonomous Locomotion and Manipulation for Disaster Response with a Centaur-Like Robot
2018
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2018 pre-print arXiv:1809.06802v1 fatcat:2ccfxgvvvbg4xkeq2b6fclug2iOther Versions
We developed a set of operator assistance functionalities with different levels of autonomy to control the robot for challenging locomotion and manipulation tasks. ...
Mobile manipulation tasks are one of the key challenges in the field of search and rescue (SAR) robotics requiring robots with flexible locomotion and manipulation abilities. ...
We transfer This transfer happens as the result of a non-rigid registration method based on a learned latent shape space. ...
doi:10.1109/iros.2018.8594509
dblp:conf/iros/KlamtRSLPDB18
fatcat:srrn5u4ru5brffjs7odl53s3qq
Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control
[article]
2018
arXiv
pre-print
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We demonstrate that visual MPC can generalize to never-before-seen objects---both rigid and deformable---and solve a range of user-defined object manipulation tasks using the same model. ...
Deep reinforcement learning (RL) algorithms can learn complex robotic skills from raw sensory inputs, but have yet to achieve the kind of broad generalization and applicability demonstrated by deep learning ...
To the best of our knowledge this is the first algorithm for robotic manipulation handling both rigid and deformable objects. ...
arXiv:1812.00568v1
fatcat:iitxl2ew3ngvhiaci7istdxhbu
kPAM: KeyPoint Affordances for Category-Level Robotic Manipulation
[article]
2019
arXiv
pre-print
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We would like robots to achieve purposeful manipulation by placing any instance from a category of objects into a desired set of goal states. ...
Extensive hardware experiments demonstrate our method can reliably accomplish tasks with never-before seen objects in a category, such as placing shoes and mugs with significant shape variation into category ...
learning
with applications to robotic manipulation. ...
arXiv:1903.06684v2
fatcat:gaghpp3ukjg7xad3u35yplur24
2019 Index IEEE Robotics and Automation Letters Vol. 4
2019
IEEE Robotics and Automation Letters
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., +, LRA Oct. 2019 3742-3749
Learning to Serve: An Experimental Study for a New Learning From
Demonstrations Framework. ...
., +, LRA April 2019 1948-1955 3-D Deformable Object Manipulation Using Deep Neural Networks. ...
doi:10.1109/lra.2019.2955867
fatcat:ckastwefh5chhamsravandtnx4
Deep reinforcement learning in medical imaging: A literature review
[article]
2021
arXiv
pre-print
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Deep reinforcement learning (DRL) augments the reinforcement learning framework, which learns a sequence of actions that maximizes the expected reward, with the representative power of deep neural networks ...
We then cover existing DRL applications for medical imaging, which are roughly divided into three main categories: (I) parametric medical image analysis tasks including landmark detection, object/lesion ...
class of non-rigid registration.Krebs et al. (2017) extend the artificial agent approach to handle non-rigid registration. ...
arXiv:2103.05115v1
fatcat:ocr6kq7atnbhxazj7twvvhl5uy
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