Publikationer av Danica Kragic Jensfelt
Refereegranskade
Artiklar
[1]
H. Xia et al., "Shaping high-performance wearable robots for human motor and sensory reconstruction and enhancement," Nature Communications, vol. 15, no. 1, 2024.
[2]
M. Lippi et al., "Enabling Visual Action Planning for Object Manipulation Through Latent Space Roadmap," IEEE Transactions on robotics, vol. 39, no. 1, s. 57-75, 2023.
[3]
A. A. Medbouhi et al., "InvMap and Witness Simplicial Variational Auto-Encoders," MACHINE LEARNING AND KNOWLEDGE EXTRACTION, vol. 5, no. 1, s. 199-236, 2023.
[4]
W. Yin et al., "Multimodal dance style transfer," Machine Vision and Applications, vol. 34, no. 4, 2023.
[5]
K. Kaya et al., "Programmable Matter with Free and High-Resolution Transfiguration and Locomotion," Advanced Functional Materials, 2023.
[6]
O. Gustavsson et al., "Cloth manipulation based on category classification and landmark detection," International Journal of Advanced Robotic Systems, vol. 19, no. 4, 2022.
[7]
S. Ahlberg et al., "Co-adaptive Human-Robot Cooperation : Summary and Challenges," Unmanned Systems, vol. 10, no. 02, s. 187-203, 2022.
[8]
A. Maki et al., "In Memoriam : Jan-Olof Eklundh," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 9, s. 4488-4489, 2022.
[9]
A. Ghadirzadeh et al., "Training and Evaluation of Deep Policies Using Reinforcement Learning and Generative Models," Journal of machine learning research, vol. 23, 2022.
[10]
A. Czeszumski et al., "Coordinating With a Robot Partner Affects Neural Processing Related to Action Monitoring," Frontiers in Neurorobotics, vol. 15, 2021.
[11]
H. Song et al., "Herding by caging : a formation-based motion planning framework for guiding mobile agents," Autonomous Robots, vol. 45, no. 5, s. 613-631, 2021.
[12]
A. Ghadirzadeh et al., "Human-Centered Collaborative Robots With Deep Reinforcement Learning," IEEE Robotics and Automation Letters, vol. 6, no. 2, s. 566-571, 2021.
[13]
S. Abdul Khader et al., "Learning deep energy shaping policies for stability-guaranteed manipulation," IEEE Robotics and Automation Letters, vol. 6, no. 4, s. 8583-8590, 2021.
[14]
H. Yin, A. Varava och D. Kragic, "Modeling, learning, perception, and control methods for deformable object manipulation," Science Robotics, vol. 6, no. 54, 2021.
[15]
M. C. Welle et al., "Partial caging : a clearance-based definition, datasets, and deep learning," Autonomous Robots, vol. 45, no. 5, s. 647-664, 2021.
[16]
S. A. Khader et al., "Stability-Guaranteed Reinforcement Learning for Contact-Rich Manipulation," IEEE Robotics and Automation Letters, vol. 6, no. 1, s. 1-8, 2021.
[17]
O. Kravchenko et al., "A Robotics-Inspired Screening Algorithm for Molecular Caging Prediction," Journal of Chemical Information and Modeling, vol. 60, no. 3, s. 1302-1316, 2020.
[18]
I. Garcia-Camacho et al., "Benchmarking Bimanual Cloth Manipulation," IEEE Robotics and Automation Letters, vol. 5, no. 2, s. 1111-1118, 2020.
[19]
S. Cruciani et al., "Benchmarking In-Hand Manipulation," IEEE Robotics and Automation Letters, vol. 5, no. 2, s. 588-595, 2020.
[20]
G. -. Yang et al., "Combating COVID-19-The role of robotics in managing public health and infectious diseases," Science Robotics, vol. 5, no. 40, 2020.
[21]
S. Abdul Khader et al., "Data-Efficient Model Learning and Prediction for Contact-Rich Manipulation Tasks," IEEE Robotics and Automation Letters, vol. 5, no. 3, s. 4321-4328, 2020.
[22]
A. Varava et al., "Free Space of Rigid Objects : Caging, Path Non-existence, and Narrow Passage Detection," Springer Proceedings in Advanced Robotics, vol. 14, s. 19-35, 2020.
[23]
A. Varava et al., "Free space of rigid objects : caging, path non-existence, and narrow passage detection," The international journal of robotics research, 2020.
[24]
J. Bütepage et al., "Imitating by Generating : Deep Generative Models for Imitation of Interactive Tasks," Frontiers in Robotics and AI, vol. 7, 2020.
[25]
M. Kokic, D. Kragic och J. Bohg, "Learning Task-Oriented Grasping From Human Activity Datasets," IEEE Robotics and Automation Letters, vol. 5, no. 2, s. 3352-3359, 2020.
[26]
V. E. Arriola-Rios et al., "Modeling of Deformable Objects for Robotic Manipulation : A Tutorial and Review," Frontiers in Robotics and AI, vol. 7, 2020.
[27]
Y. Bekiroglu et al., "Visual and tactile 3D point cloud data from real robots for shape modeling and completion," Data in Brief, vol. 30, 2020.
[28]
W. Yuan et al., "End-to-end nonprehensile rearrangement with deep reinforcement learning and simulation-to-reality transfer," Robotics and Autonomous Systems, vol. 119, s. 119-134, 2019.
[29]
J. Butepage et al., "From Visual Understanding to Complex Object Manipulation," Annual Review of Control, Robotics, and Autonomous Systems, vol. 2, s. 161-179, 2019.
[30]
K. Hang et al., "Perching and resting-A paradigm for UAV maneuvering with modularized landing gears," SCIENCE ROBOTICS, vol. 4, no. 28, 2019.
[31]
A. Billard och D. Kragic, "Trends and challenges in robot manipulation," Science, vol. 364, no. 6446, s. 1149-+, 2019.
[32]
J. F. Carvalho et al., "An algorithm for calculating top-dimensional bounding chains," PEERJ COMPUTER SCIENCE, 2018.
[33]
K. Hang et al., "A Framework for Optimal Grasp Contact Planning," IEEE Robotics and Automation Letters, vol. 2, no. 2, s. 704-711, 2017.
[34]
F. T. Pokorny et al., "A database for reproducible manipulation research : CapriDB – Capture, Print, Innovate," Data in Brief, vol. 11, s. 491-498, 2017.
[35]
J. Bohg et al., "Interactive Perception : Leveraging Action in Perception and Perception in Action," IEEE Transactions on robotics, vol. 33, no. 6, s. 1273-1291, 2017.
[36]
V. Högman et al., "A sensorimotor learning framework for object categorization," IEEE Transactions on Cognitive and Developmental Systems, vol. 8, no. 1, s. 15-25, 2016.
[37]
Y. Karayiannidis et al., "An Adaptive Control Approach for Opening Doors and Drawers Under Uncertainties," IEEE Transactions on robotics, vol. 32, no. 1, s. 161-175, 2016.
[38]
A. Varava, D. Kragic och F. T. Pokorny, "Caging Grasps of Rigid and Partially Deformable 3-D Objects With Double Fork and Neck Features," IEEE Transactions on robotics, vol. 32, no. 6, s. 1479-1497, 2016.
[39]
M. Li et al., "Dexterous grasping under shape uncertainty," Robotics and Autonomous Systems, vol. 75, s. 352-364, 2016.
[40]
K. Hang et al., "Hierarchical Fingertip Space : A Unified Framework for Grasp Planning and In-Hand Grasp Adaptation," IEEE Transactions on robotics, vol. 32, no. 4, s. 960-972, 2016.
[41]
T. Feix et al., "The GRASP Taxonomy of Human Grasp Types," IEEE Transactions on Human-Machine Systems, vol. 46, no. 1, s. 66-77, 2016.
[42]
M. Vejdemo Johansson et al., "Cohomological learning of periodic motion," Applicable Algebra in Engineering, Communication and Computing, vol. 26, no. 1-2, s. 5-26, 2015.
[43]
D. Song et al., "Task-Based Robot Grasp Planning Using Probabilistic Inference," IEEE Transactions on robotics, vol. 31, no. 3, s. 546-561, 2015.
[44]
J. Bohg et al., "Data-Driven Grasp Synthesis-A Survey," IEEE Transactions on robotics, vol. 30, no. 2, s. 289-309, 2014.
[45]
A. Drimus et al., "Design of a flexible tactile sensor for classification of rigid and deformable objects," Robotics and Autonomous Systems, vol. 62, no. 1, s. 3-15, 2014.
[46]
M. Björkman, N. Bergström och D. Kragic, "Detecting, segmenting and tracking unknown objects using multi-label MRF inference," Computer Vision and Image Understanding, vol. 118, s. 111-127, 2014.
[47]
M. Patel et al., "Learning object, grasping and manipulation activities using hierarchical HMMs," Autonomous Robots, vol. 37, no. 3, s. 317-331, 2014.
[48]
T. Feix et al., "A Metric for Comparing the Anthropomorphic Motion Capability of Artificial Hands," IEEE Transactions on robotics, vol. 29, no. 1, s. 82-93, 2013.
[49]
J. Romero et al., "Extracting Postural Synergies for Robotic Grasping," IEEE Transactions on robotics, vol. 29, no. 6, s. 1342-1352, 2013.
[50]
J. Romero et al., "Non-parametric hand pose estimation with object context," Image and Vision Computing, vol. 31, no. 8, s. 555-564, 2013.
[51]
C. Smith et al., "Dual arm manipulation-A survey," Robotics and Autonomous Systems, vol. 60, no. 10, s. 1340-1353, 2012.
[52]
G. Kootstra et al., "Enabling grasping of unknown objects through a synergistic use of edge and surface information," The international journal of robotics research, vol. 31, no. 10, s. 1190-1213, 2012.
[53]
D. Kragic och G. D. Hager, "Special Issue on Robotic Vision," The international journal of robotics research, vol. 31, no. 4, s. 379-380, 2012.
[54]
G. Kootstra et al., "VisGraB : A Benchmark for Vision-Based Grasping," Paladyn - Journal of Behavioral Robotics, vol. 3, no. 2, s. 54-62, 2012.
[55]
X. Gratal et al., "Visual servoing on unknown objects," Mechatronics (Oxford), vol. 22, no. 4, s. 423-435, 2012.
[56]
Y. Bekiroglu et al., "Assessing Grasp Stability Based on Learning and Haptic Data," IEEE Transactions on robotics, vol. 27, no. 3, s. 616-629, 2011.
[57]
A. Nazem et al., "Interfacing a parallel simulation of a neuronal network to robotic hardware using MUSIC, with application to real-time figure-ground segregation.," BMC Neuroscience, vol. 12, no. Suppl 1, s. 78-78, 2011.
[58]
A. Nazem et al., "Parallel implementation of a biologically inspired model of figure-ground segregation : Application to real-time data using MUSIC," Frontiers in Neuroinformatics, 2011.
[59]
. Sanmohan et al., "Primitive-Based Action Representation and Recognition," Advanced Robotics, vol. 25, no. 6-7, s. 871-891, 2011.
[60]
V. Kyrki och D. Kragic, "Tracking rigid objects using integration of model-based and model-free cues," Machine Vision and Applications, vol. 22, no. 2, s. 323-335, 2011.
[61]
H. Kjellström, J. Romero och D. Kragic, "Visual object-action recognition : Inferring object affordances from human demonstration," Computer Vision and Image Understanding, vol. 115, no. 1, s. 81-90, 2011.
[62]
M. Popovic et al., "A strategy for grasping unknown objects based on co-planarity and colour information," Robotics and Autonomous Systems, vol. 58, no. 5, s. 551-565, 2010.
[63]
B. Rasolzadeh et al., "An Active Vision System for Detecting, Fixating and Manipulating Objects in the Real World," The international journal of robotics research, vol. 29, no. 2-3, s. 133-154, 2010.
[64]
V. Kruger et al., "Learning Actions from observations Primitive-Based Modeling and Grammar," IEEE robotics & automation magazine, vol. 17, no. 2, s. 30-43, 2010.
[65]
J. Bohg och D. Kragic, "Learning grasping points with shape context," Robotics and Autonomous Systems, vol. 58, no. 4, s. 362-377, 2010.
[66]
D. Kragic och M. Vincze, "Vision for Robotics," Foundations and Trends in Robotics, vol. 1, no. 1, s. 1-78, 2010.
[67]
N. Kruger et al., "A Formal Definition of Object-Action Complexes and Examples at Different Levels of the Processing Hierarchy," Computer and Information Science, s. 1-39, 2009.
[68]
D. Kraft et al., "BIRTH OF THE OBJECT : DETECTION OF OBJECTNESS AND EXTRACTION OF OBJECT SHAPE THROUGH OBJECT-ACTION COMPLEXES (vol 5, pg 247, 2008)," International Journal of Humanoid Robotics, vol. 6, no. 3, s. 561-561, 2009.
[69]
M. Karasalo et al., "Contour Reconstruction using Recursive Smoothing Splines - Algorithms and Experimental Validation," Robotics and Autonomous Systems, vol. 57, no. 6-7, s. 617-628, 2009.
[70]
J. Tegin et al., "Demonstration-based learning and control for automatic grasping," Intelligent Service Robotics, vol. 2, no. 1, s. 23-30, 2009.
[71]
K. Sjöö et al., "Object Search and Localization for an Indoor Mobile Robot," Journal of Computing and Information Technology, vol. 17, no. 1, s. 67-80, 2009.
[72]
J. Bohg et al., "TOWARDS GRASP-ORIENTED VISUAL PERCEPTION FOR HUMANOID ROBOTS," INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS, vol. 6, no. 3, s. 387-434, 2009.
[73]
D. Kraft et al., "Birth Of The Object : Detection Of Objectness And Extraction Of Object Shape Through Object-Action Complexes," International Journal of Humanoid Robotics, vol. 5, no. 2, s. 247-265, 2008.
[74]
D. Aarno och D. Kragic, "Motion intention recognition in robot assisted applications," Robotics and Autonomous Systems, vol. 56, no. 8, s. 692-705, 2008.
[75]
S. Ekvall och D. Kragic, "Robot Learning from Demonstration : A Task-level Planning Approach," , vol. 5, no. 3, s. 223-234, 2008.
[76]
G. Lopez-Nicolas et al., "Switching visual control based on epipoles for mobile robots," Robotics and Autonomous Systems, vol. 56, no. 7, s. 592-603, 2008.
[77]
I. S. Vicente et al., "Action recognition and understanding through motor primitives," Advanced Robotics, vol. 21, no. 15, s. 1687-1707, 2007.
[78]
J. Robison Fernlund, R. W. Zimmerman och D. Kragic, "Influence of volume/mass on grain-size curves and conversion of image-analysis size to sieve size," Engineering Geology, vol. 90, no. 04-mar, s. 124-137, 2007.
[79]
S. Ekvall, D. Kragic och P. Jensfelt, "Object detection and mapping for service robot tasks," Robotica (Cambridge. Print), vol. 25, s. 175-187, 2007.
[80]
V. Krueger et al., "The meaning of action : a review on action recognition and mapping," Advanced Robotics, vol. 21, no. 13, s. 1473-1501, 2007.
[81]
V. Kyrki, D. Kragic och H. I. Christensen, "Measurement errors in visual servoing," Robotics and Autonomous Systems, vol. 54, no. 10, s. 815-827, 2006.
[82]
S. Ekvall, D. Aarno och D. Kragic, "Online task recognition and real-time adaptive assistance for computer-aided machine control," IEEE Transactions on robotics, vol. 22, no. 5, s. 1029-1033, 2006.
[83]
D. Kragic et al., "Human-machine collaborative systems for microsurgical applications," The international journal of robotics research, vol. 24, no. 9, s. 731-741, 2005.
[84]
S. Ekvall och D. Kragic, "Integrating Object and Grasp Recognition for Dynamic Scene interpretation," Advanced Robotics, 2005.
[85]
S. Ekvall, D. Kragic och F. Hoffmann, "Object recognition and pose estimation using color cooccurrence histograms and geometric modeling," Image and Vision Computing, vol. 23, no. 11, s. 943-955, 2005.
[86]
D. Kragic et al., "Vision for robotic object manipulation in domestic settings," Robotics and Autonomous Systems, vol. 52, no. 1, s. 85-100, 2005.
[87]
D. Kragic, "Modelling, Specification and Robustness Issues for Robotic Manipulation Tasks," International Journal of Advanced Robotic Systems, vol. 1, no. 2, s. 75-86, 2004.
[88]
D. Kragic och H. I. Christensen, "Robust visual servoing," The international journal of robotics research, vol. 22, no. 11-Oct, s. 923-939, 2003.
[89]
D. Kragic, L. Petersson och H. I. Christensen, "Visually guided manipulation tasks," Robotics and Autonomous Systems, vol. 40, no. 3-Feb, s. 193-203, 2002.
[90]
D. Kragic och H. I. Christensen, "Cue integration for visual servoing," IEEE transactions on robotics and automation, vol. 17, no. 1, s. 18-27, 2001.
[91]
D. Kragic, "Visual Servoing for Manipulation: Robustness and Integration Issues," IEEE transactions on robotics and automation, s. 230, 2001.
[92]
[93]
D. Kragic och H. I. Christensen, "Integration of visual cues for active tracking of an end-effector," , s. 362-368, 1999.
Konferensbidrag
[94]
W. Yin et al., "Scalable Motion Style Transfer with Constrained Diffusion Generation," i The 38th Annual AAAI Conference on Artificial Intelligence, February 20-27, 2024, Vancouver, Canada, 2024.
[95]
M. Moletta et al., "A Virtual Reality Framework for Human-Robot Collaboration in Cloth Folding," i 2023 IEEE-RAS 22nd International Conference on Humanoid Robots, 2023.
[96]
G. L. Marchetti et al., "An Efficient and Continuous Voronoi Density Estimator," i Proceedings of the 26th International Conference on Artificial Intelligence and Statistics, AISTATS 2023, 2023, s. 4732-4744.
[97]
W. Yin et al., "Controllable Motion Synthesis and Reconstruction with Autoregressive Diffusion Models," i 2023 32ND IEEE INTERNATIONAL CONFERENCE ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION, RO-MAN, 2023, s. 1102-1108.
[98]
W. Yin et al., "Dance Style Transfer with Cross-modal Transformer," i 2023 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV), 2023, s. 5047-5056.
[99]
N. Rajabi et al., "Detecting the Intention of Object Handover in Human-Robot Collaborations : An EEG Study," i 2023 32ND IEEE INTERNATIONAL CONFERENCE ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION, RO-MAN, 2023, s. 549-555.
[100]
A. Longhini et al., "EDO-Net : Learning Elastic Properties of Deformable Objects from Graph Dynamics," i Proceedings - ICRA 2023 : IEEE International Conference on Robotics and Automation, 2023, s. 3875-3881.
[101]
A. Longhini et al., "Elastic Context : Encoding Elasticity for Data-driven Models of Textiles," i Proceedings - ICRA 2023 : IEEE International Conference on Robotics and Automation, 2023, s. 1764-1770.
[102]
L. A. Pérez Rey et al., "Equivariant Representation Learning in the Presence of Stabilizers," i Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, ECML PKDD 2023, Proceedings, 2023, s. 693-708.
[103]
G. L. Marchetti et al., "Equivariant Representation Learning via Class-Pose Decomposition," i Proceedings of the 26th International Conference on Artificial Intelligence and Statistics, AISTATS 2023, 2023, s. 4745-4756.
[104]
S. van Waveren et al., "Generating Scenarios from High-Level Specifications for Object Rearrangement Tasks," i 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023), October 1–5, 2023, Detroit, Michigan, USA, 2023.
[105]
S. Van Waveren et al., "Generating Scenarios from High-Level Specifications for Object Rearrangement Tasks," i 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023, 2023, s. 11420-11427.
[106]
Z. Weng et al., "GoNet : An Approach-Constrained Generative Grasp Sampling Network," i 2023 IEEE-RAS 22nd International Conference on Humanoid Robots, 2023.
[107]
A. Reichlin et al., "Learning Geometric Representations of Objects via Interaction," i Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, ECML PKDD 2023, Proceedings, 2023, s. 629-644.
[108]
N. Rajabi et al., "Mental Face Image Retrieval Based on a Closed-Loop Brain-Computer Interface," i Augmented Cognition : 17th International Conference, AC 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Proceedings, 2023, s. 26-45.
[109]
S. Sabzevari et al., "PG-3DVTON : Pose-Guided 3D Virtual Try-on Network," i VISIGRAPP 2023 - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Volume 4, 2023, s. 819-829.
[110]
A. Kravchenko et al., "Active Nearest Neighbor Regression Through Delaunay Refinement," i Proceedings of the 39th International Conference on Machine Learning, 2022, s. 11650-11664.
[111]
M. Lippi et al., "Augment-Connect-Explore : a Paradigm for Visual Action Planning with Data Scarcity," i 2022 IEEE/RSJ international conference on intelligent robots and systems (IROS), 2022, s. 754-761.
[112]
A. Reichlin et al., "Back to the Manifold : Recovering from Out-of-Distribution States," i 2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2022, s. 8660-8666.
[113]
C. Chamzas et al., "Comparing Reconstruction- and Contrastive-based Models for Visual Task Planning," i 2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2022, s. 12550-12557.
[114]
H. Yin, C. K. Verginis och D. Kragic, "Consensus-based Normalizing-Flow Control : A Case Study in Learning Dual-Arm Coordination," i 2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2022, s. 10417-10424.
[115]
P. Poklukar et al., "Delaunay Component Analysis for Evaluation of Data Representations," i Proceedings 10th International Conference on Learning Representations, ICLR 2022, 2022.
[116]
H. Yin, M. C. Welle och D. Kragic, "Embedding Koopman Optimal Control in Robot Policy Learning," i 2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2022, s. 13392-13399.
[117]
P. Poklukar et al., "GMC - Geometric Multimodal Contrastive Representation Learning," i International Conference on Machine Learning, 2022.
[118]
P. Poklukar et al., "Geometric Multimodal Contrastive Representation Learning," i Proceedings of the 39th International Conference on Machine Learning, ICML 2022, 2022, s. 17782-17800.
[119]
S. U. Demir Kanik et al., "Improving EEG-based Motor Execution Classification for Robot Control," i Proceedings 14th International Conference, SCSM 2022, Held as Part of the 24th HCI International Conference, HCII 2022 : Social Computing and Social Media: Design, User Experience and Impact, 2022, s. 65-82.
[120]
P. Tajvar et al., "Robust motion planning for non-holonomic robots with planar geometric constraints," i Robotics Research : The 19th International Symposium ISRR, 2022, s. 850-866.
[121]
V. Polianskii et al., "Voronoi Density Estimator for High-Dimensional Data : Computation, Compactification and Convergence," i Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, 2022, s. 1644-1653.
[122]
A. Ghadirzadeh et al., "Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms," i 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, s. 1274-1280.
[123]
P. Poklukar, A. Varava och D. Kragic, "GeomCA : Geometric Evaluation of Data Representations," i International Conference On Machine Learning, Vol 139, 2021.
[124]
P. Poklukar, A. Varava och D. Kragic, "GeomCA: Geometric Evaluation of Data Representations," i Proceedings of Machine Learning Research : Proceedings of the 38th International Conference on Machine Learning, 2021, s. 8588-8598.
[125]
W. Yin et al., "Graph-based Normalizing Flow for Human Motion Generation and Reconstruction," i 2021 30th IEEE international conference on robot and human interactive communication (RO-MAN), 2021, s. 641-648.
[126]
Z. Weng et al., "Graph-based Task-specific Prediction Models for Interactions between Deformable and Rigid Objects," i 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, s. 5741-5748.
[127]
I. Mitsioni et al., "Interpretability in Contact-Rich Manipulation via Kinodynamic Images," i 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021, s. 10175-10181.
[128]
G. L. Marchetti et al., "Learning Coarsened Dynamic Graph Representations for Deformable Object Manipulation," i 2021 20Th International Conference On Advanced Robotics (ICAR), 2021, s. 955-960.
[129]
S. Abdul Khader et al., "Learning Stable Normalizing-Flow Control for Robotic Manipulation," i 2021 IEEE International Conference On Robotics And Automation (ICRA 2021), 2021, s. 1644-1650.
[130]
F. Esposito et al., "Learning Task Constraints in Visual-Action Planning from Demonstrations," i 30th IEEE International Conference on Robot & Human Interactive Communication, RO-MAN 2021, 2021, s. 131-138.
[131]
I. Mitsioni, Y. Karayiannidis och D. Kragic, "Modelling and Learning Dynamics for Robotic Food-Cutting," i 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), 2021, s. 1194-1200.
[132]
I. Mitsioni et al., "Safe Data-Driven Contact-Rich Manipulation," i Proceedings of the 2020 IEEE-RAS 20th international conference on humanoid robots (Humanoids 2020), 2021, s. 120-127.
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R. Antonova et al., "Sequential Topological Representations for Predictive Models of Deformable Objects," i Proceedings of the 3rd Conference on Learning for Dynamics and Control, L4DC 2021, 2021, s. 348-360.
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A. Longhini et al., "Textile Taxonomy and Classification Using Pulling and Twisting," i 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : Prague/Online 27.09-01.10.2021, 2021, s. 7541-7548.
[135]
J. Karlsson et al., "When to Terminate : Path Non-existence Verification Improves Sampling-based Motion Planning," i 2021 20Th International Conference On Advanced Robotics (ICAR), 2021, s. 594-600.
[136]
R. Antonova et al., "Bayesian optimization in variational latent spaces with dynamic compression," i Proceedings of Machine Learning Research : Volume 100: Proceedings of the 3rd Annual Conference on Robot Learning (CoRL), 2020, s. 456-465.
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A. Varava et al., "Caging and Path Non-existence : A Deterministic Sampling-Based Verification Algorithm," i Robotics Research, 2020, s. 589-604.
[138]
S. Cruciani et al., "Discrete Bimanual Manipulation for Wrench Balancing," i 2020 IEEE International Conference on Robotics and Automation, ICRA 2020, 2020, s. 2631-2637.
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T. Ziegler et al., "Fashion Landmark Detection and Category Classification for Robotics," i Proceedings IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC 2020), 2020.
[140]
S. Cruciani, H. Yin och D. Kragic, "In-Hand Manipulation of Objects with Unknown Shapes," i 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, s. 8848-8854.
[141]
M. Lippi et al., "Latent Space Roadmap for Visual Action Planning of Deformable and Rigid Object Manipulation," i 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, s. 5619-5626.
[142]
H. Song et al., "Multi-Object Rearrangement with Monte Carlo Tree Search : A Case Study on Planar Nonprehensile Sorting," i International Conference on Intelligent Robots and Systems (IROS), On-Demand Conference, October 25 - November 25, 2020, 2020.
[143]
I. Mitsioni et al., "Safe Data-Driven Contact-Rich Manipulation," i IEEE-RAS International Conference on Humanoid Robots, 2020.
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M. Björkman och D. Kragic, "Active 3D Segmentation through Fixation of Previously Unseen Objects," i British Machine Vision Conference (BMVC), Aberystwyth, UK, 2010, s. 119.1-119.11.
[281]
M. Björkman och D. Kragic, "Active 3D scene segmentation and detection of unknown objects," i IEEE International Conference on Robotics and Automation (ICRA), Anchorage, USA, 2010, s. 3114-3120.
[282]
N. Bergström et al., "Active Scene Analysis," i RSS Workshop on Towards Closing the Loop: Active Learning for Robotics. Univeridad de Zaragoza, Spain. June 27th 2010, 2010.
[283]
M. Johnson-Roberson et al., "Attention-based Active 3D Point Cloud Segmentation," i IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), 2010, s. 1165-1170.
[284]
M. Johnson-Roberson et al., "Enhanced visual scene understanding through human-robot dialog," i Dialog with Robots : AAAI 2010 Fall Symposium, 2010.
[285]
J. Laaksonen, V. Kyrki och D. Kragic, "Evaluation of feature representation and machine learning methods in grasp stability learning," i 2010 10th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2010, 2010, s. 112-117.
[286]
C. H. Ek et al., "Exploring affordances in robot grasping through latent structure representation," i The 11th European Conference on Computer Vision (ECCV 2010), 2010.
[287]
G. Kootstra, N. Bergström och D. Kragic, "Fast and Automatic Detection and Segmentation of Unknown Objects," i Proceedings of the 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2010, s. 442-447.
[288]
K. Hübner och D. Kragic, "Grasping by parts : Robot grasp generation from 3D box primitives," i 4th International Conference on Cognitive Systems, CogSys 2010, 2010.
[289]
J. Romero, H. Kjellström och D. Kragic, "Hands in Action : Real-Time 3D Reconstruction of Hands in Interaction with Objects," i 2010 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 2010, s. 458-463.
[290]
D. Song et al., "Learning Task Constraints for Robot Grasping using Graphical Models," i IEEE/RSJ International Conference on Intelligent RObots and Systems, 2010.
[291]
Y. Bekiroglu et al., "Learning grasp stability based on haptic data," i RSS 2010 workshop: Representations for object grasping and manipulation in single and dual arm tasks. Zaragoza, Spain. 28.06.2010 - 28.06.2010, 2010.
[292]
Y. Bekiroglu, D. Kragic och V. Kyrki, "Learning grasp stability based on tactile data and HMMs," i IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2010, 2010, s. 132-137.
[293]
S. Geidenstam et al., "Learning of 2D grasping strategies from box-based 3D object approximations," i Robotics : Science and Systems, 2010, s. 9-16.
[294]
D. Song et al., "Learning task constraints in robot grasping," i 4th International Conference on Cognitive Systems, CogSys 2010, 2010.
[295]
O. J. Rubio, K. Hübner och D. Kragic, "Representations for Object Grasping and Learning from Experience," i IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), 2010, s. 1566-1571.
[296]
X. Gratal et al., "Scene Representation and Object Grasping Using Active Vision," i IROS’10 Workshop on Defining and Solving Realistic Perception Problems in Personal Robotics, Taipei, Taiwan, 2010., 2010.
[297]
J. Romero et al., "Spatio-Temporal Modeling of Grasping Actions," i IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), 2010, s. 2103-2108.
[298]
J. Bohg et al., "Strategies for Multi-Modal Scene Exploration," i IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), 2010, s. 4509-4515.
[299]
C. H. Ek et al., "Task Modeling in Imitation Learning using Latent Variable Models," i 2010 10th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2010, 2010, s. 458-553.
[300]
H. Kjellström, D. Kragic och M. J. Black, "Tracking People Interacting with Objects," i 2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2010, s. 747-754.
[301]
. Sanmohan, V. Krüger och D. Kragic, "Unsupervised learning of action primitives," i 2010 10th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2010, 2010, s. 554-559.
[302]
G. Kootstra, N. Bergström och D. Kragic, "Using Symmetry to Select Fixation Points for Segmentation," i Proceedings of the 20th International Conference on Pattern Recognition, 2010, s. 3894-3897.
[303]
M. Do et al., "Grasp recognition and mapping on humanoid robots," i 9th IEEE-RAS International Conference on Humanoid Robots, HUMANOIDS09, 2009, s. 465-471.
[304]
J. Bohg och D. Kragic, "Grasping Familiar Objects using Shape Context," i ICAR : 2009 14th International Conference on Advanced Robotics, 2009, s. 50-55.
[305]
K. Hübner et al., "Grasping Known Objects with Humanoid Robots : A Box-Based Approach," i 2009 International Conference on Advanced Robotics, ICAR 2009, 2009, s. 179-184.
[306]
N. Bergström, J. Bohg och D. Kragic, "Integration of Visual Cues for Robotic Grasping," i COMPUTER VISION SYSTEMS, PROCEEDINGS, 2009, s. 245-254.
[307]
C. Barck-Holst et al., "Learning Grasping Affordance Using Probabilistic and Ontological Approaches," i 2009 International Conference on Advanced Robotics, ICAR 2009, 2009, s. 96-101.
[308]
J. Romero, H. Kjellström och D. Kragic, "Modeling and Evaluation of Human-to-Robot Mapping of Grasps," i ICAR : 2009 International Conference on Advanced Robotics, 2009, s. 228-233.
[309]
J. Romero, H. Kjellström och D. Kragic, "Monocular Real-Time 3D Articulated Hand Pose Estimation," i 9th IEEE-RAS International Conference on Humanoid Robots, HUMANOIDS09, 2009, s. 87-92.
[310]
J. Tegin et al., "Real Life Grasping using an Under-actuated Robot Hand - Simulation and Experiments," i ICAR : 2009 14th International Conference on Advanced Robotics, 2009, s. 366-373.
[311]
J. Romero et al., "Dynamic Time Warping for binocular hand tracking and reconstruction," i 2008 IEEE International Conference On Robotics And Automation : Vols 1-9, 2008, s. 2289-2294.
[312]
D. Aarno et al., "Early reactive grasping with second order 3D feature relations," i Recent Progress In Robotics: Viable Robotic Service To Human, 2008, s. 91-105.
[313]
J. Romero, H. Kjellström och D. Kragic, "Human-to-Robot Mapping of Grasps," i 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Nice, France. September 22 - 26 2008, 2008.
[314]
K. Hübner et al., "Integration of visual and shape attributes for object action complexes," i Computer Vision Systems, Proceedings, 2008, s. 13-22.
[315]
K. Hübner, S. Ruthotto och D. Kragic, "Minimum Volume Bounding Box decomposition for shape approximation in robot grasping," i 2008 IEEE International Conference on Robotics and Automation, ICRA 2008 : Vols 1-9, 2008, s. 1628-1633.
[316]
D. Martinez och D. Kragic, "Modeling and recognition of actions through motor primitives," i 2008 IEEE International Conference On Robotics And Automation : Vols 1-9, 2008, s. 1704-1709.
[317]
K. Hübner och D. Kragic, "Selection of Robot Pre-Grasps using Box-Based Shape Approximation," i 2008 IEEE/RSJ International Conference On Robots And Intelligent Systems, Vols 1-3, Conference Proceedings, 2008, s. 1765-1770.
[318]
H. Kjellström et al., "Simultaneous Visual Recognition of Manipulation Actions and Manipulated Objects," i Computer Vision - Eccv 2008, Pt Ii, Proceedings, 2008, s. 336-349.
[319]
H. Kjellström, J. Romero och D. Kragic, "Visual Recognition of Grasps for Human-to-Robot Mapping," i 2008 IEEE/RSJ International Conference On Robots And Intelligent Systems, Vols 1-3, Conference Proceedings, 2008, s. 3192-3199.
[320]
V. Kyrki et al., "Action Recognition and Understanding using Motor Primitives," i 2007 RO-MAN : 16TH IEEE INTERNATIONAL SYMPOSIUM ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION, 2007, s. 1113-1118.
[321]
G. Piccolo et al., "Contour reconstruction using recursive smoothing splines experimental validation," i IEEE International Conference on Intelligent Robots and Systems : Vols 1-9, 2007, s. 2077-2082.
[322]
S. Ekvall och D. Kragic, "Learning and evaluation of the approach vector for automatic grasp generation and planning," i Proceedings - IEEE International Conference on Robotics and Automation : Vols 1-10, 2007, s. 4715-4720.
[323]
I. S. Vicente, D. Kragic och J.-O. Eklundh, "Learning and recognition of object manipulation actions using linear and nonlinear dimensionality reduction," i 2007 RO-MAN : 16TH IEEE International Symposium On Robot And Human Interactive Communication, Vols 1-3, 2007, s. 1003-1008.
[324]
P. Jensfelt et al., "A framework for vision based bearing only 3D SLAM," i Proceedings of the 2006 IEEE International Conference on Robotics and Automation, Orlando, Florida - May 2006 : Vols 1-10, 2006, s. 1944-1950.
[325]
P. Jensfelt et al., "Augmenting slam with object detection in a service robot framework," i Proceedings, IEEE International Workshop on Robot and Human Interactive Communication, 2006, s. 741-746.
[326]
P. Jensfelt et al., "Exploiting distinguishable image features in robotic mapping and localization," i European Robotics Symposium 2006, 2006, s. 143-157.
[327]
D. Kragic och V. Kyrki, "Initialization and System Modeling in 3 D Pose Tracking," i 18th International Conference on Pattern Recognition, Vol 4, Proceedings, 2006, s. 643-646.
[328]
S. Ekvall, P. Jensfelt och D. Kragic, "Integrating active mobile robot object recognition and SLAM in natural environments," i 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vols 1-12, 2006, s. 5792-5797.
[329]
J. I. Bueno och D. Kragic, "Integration of tracking and adaptive Gaussian mixture models for posture recognition," i Proc. IEEE Int. Workshop Robot Human Interact. Commun., 2006, s. 623-628.
[330]
D. Aarno och D. Kragic, "Layered HMM for motion intention recognition," i 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vols 1-12, 2006, s. 5130-5135.
[331]
S. Ekvall och D. Kragic, "Learning Task Models from Multiple Human Demonstrations," i Robot and Human Interactive Communication, 2006. ROMAN 2006. The 15th IEEE International Symposium on Issue Date: 6-8 Sept. 2006, 2006, s. 358-363.
[332]
G. López-Nicolás et al., "Nonholonomic epipolar visual servoing," i 2006 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 2006, s. 2378-2384.
[333]
P. Preisig och D. Kragic, "Robust Statistics for 3D Object Tracking," i 2006 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), VOLS 1-10, 2006, s. 2403-2408.
[334]
D. Kragic och M. Björkman, "Strategies for object manipulation using foveal and peripheral vision," i International Conference on Computer Vision Systems (ICVS), New York, USA, 2006, s. 50.
[335]
S. Ekvall, D. Aarno och D. Kragic, "Task Learning Using Graphical Programming and Human Demonstrations," i Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, 2006, s. 398-403.
[336]
V. Kyrki och D. Kragic, "Tracking Unobservable Rotations by Cue Integration," i 2006 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 2006, s. 2744-2750.
[337]
D. Aarno, S. Ekvall och D. Kragic, "Adaptive virtual fixtures for machine-assisted teleoperation tasks," i 2005 IEEE International Conference on Robotics and Automation (ICRA), Vols 1-4, 2005, s. 1139-1144.
[338]
D. Aarno, F. Lingelbach och D. Kragic, "Constrained path planning and task-consistent path adaptation for mobile manipulators," i 2005 12th International Conference on Advanced Robotics, 2005, s. 268-273.
[339]
S. Ekvall och D. Kragic, "Grasp recognition for programming by demonstration," i 2005 IEEE International Conference on Robotics and Automation (ICRA), Vols 1-4, 2005, s. 748-753.
[340]
S. Ekvall och D. Kragic, "Integrating object and grasp recognition for dynamic scene interpretation," i 2005 12th International Conference on Advanced Robotics, 2005, s. 331-336.
[341]
V. Kyrki och D. Kragic, "Integration of Model-based and Model-free Cues for Visual Object Tracking in 3D," i 2005 IEEE International Conference on Robotics and Automation (ICRA), 2005, s. 1554-1560.
[342]
H. Cornelius, D. Kragic och J.-O. Eklundh, "Object and pose recognition using contour and shape information," i 2005 12th International Conference on Advanced Robotics, 2005, s. 613-620.
[343]
D. Kragic et al., "RETRACTED: Human-Machine Collaborative Systems for Microsurgical Applications," i Robotics Research, 2005, s. 162-171.
[344]
S. Ekvall och D. Kragic, "Receptive field cooccurrence histograms for object detection," i 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vols 1-4, 2005, s. 3969-3974.
[345]
A. I. Comport et al., "Robust Real-Time Visual Tracking : Comparison, Theoretical Analysis and Performance Evaluation," i 2005 IEEE International Conference on Robotics and Automation (ICRA), Vols 1-4, 2005, s. 2841-2846.
[346]
E. A. Topp et al., "An interactive interface for service robots," i 2004 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1- 5, PROCEEDINGS, 2004, s. 3469-3474.
[347]
D. Aarno, D. Kragic och H. Christensen, "Artificial potential biased probabilistic roadmap method," i 2004 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1- 5, PROCEEDINGS, 2004, s. 461-466.
[348]
M. Björkman och D. Kragic, "Combination of foveal and peripheral vision for object recognition and pose estimation," i 2004 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1- 5, PROCEEDINGS, 2004, s. 5135-5140.
[349]
S. Ekvall och D. Kragic, "Interactive grasp learning based on human demonstration," i 2004 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1- 5, PROCEEDINGS, 2004, s. 3519-3524.
[350]
V. Kyrki, D. Kragic och H. I. Christensen, "Measurement errors in visual servoing," i 2004 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1- 5, PROCEEDINGS, 2004, s. 1861-1867.
[351]
V. Kyrki, D. Kragic och H. I. Christensen, "New shortest-path approaches to visual servoing," i 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 28 September-2 October 2004, Sendai, Japan, 2004, s. 349-354.
[352]
D. Kragic et al., "Sensor Integration and Task Planning for Mobile Manipulation," i IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004, 2004.
[353]
D. Kragic och H. Christensen, "Biologically Motivated Visual Servoing and Grasping for Real World Tasks," i International Conference on Intelligent Robots and Systems, 2003.
[354]
D. Kragic, "Confluence of parameters in model based tracking," i 2003 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, 2003.
[355]
D. Kragic och G. Hager, "Task Modeling and Speci cation for Modular Sensory Based Human-Machine Cooperative Systems," i International Conference on Intelligent Robots and Systems, 2003.
[356]
D. Kragic et al., "Vision and tactile sensing for real world tasks," i 20th IEEE International Conference on Robotics and Automation (ICRA), 2003.
[357]
D. Kragic och H. I. Christensen, "Model Based Techniques for Robotic Servoing and Grasping," i IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002.
[358]
D. Kragic och H. I. Christensen, "Survey on Visual Servoing for Manipulation," i USENIX Technical Conference, 2002.
[359]
L. Petersson et al., "Systems integration for real–world manipulation tasks," i 2002 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS I-IV, PROCEEDINGS, 2002, s. 2500-2505.
[360]
D. Kragic och H. I. Christensen, "Weak Models and Cue Integration for Real-Time Tracking," i 19th IEEE International Conference on Robotics and Automation, 2002.
[361]
D. Kragic, A. T. Miller och P. K. Allen, "Real-time Tracking Meets Online Grasp Planning," i International Conference on Robotics and Automation, 2001.
[362]
L. Petterson, D. Austin och D. Kragic, "High-level Control of a Mobile Manipulator for Door Opening," i International Conference on Robotics and Automation, 2000.
[363]
D. Kragic och H. I. Christensen, "Tracking Techniques for Visual Servoing Tasks," i International Conference on Robotics and Automation, 2000.
[364]
H. Sidenbladh, D. Kragic och H. I. Christensen, "Person following behaviour for a mobile robot," i Proceedings - IEEE International Conference on Robotics and Automation, 1999, s. 670-675.
[365]
D. Kragic och H. I. Christensen, "Using a redundant coarsely calibrated vision system for 3d grasping," i International Conference on Computational Intelligence forModelling, Control and Automation, 1999.
Kapitel i böcker
[366]
V. Kyrki och D. Kragic, "Recent trends in computational and robot vision," i Unifying perspectives in computational and robot vision, Danica Kragic, Ville Kyrki red., New York : Springer Science+Business Media B.V., 2008, s. 1-10.
[367]
P. Jensfelt, D. Kragic och J. Folkesson, "Bearing-Only Vision SLAM with Distinguishable Image Feature," i Vision Systems Applications, Goro Obinata and Ashish Dutta red., : InTech, 2007.
[368]
D. Kragic och H. I. Christensen, "A Framework for Visual Servoing," i International Conference on Computer Vision Systems, : Springer-Verlag Berlin, 2003, s. 345-354.
[369]
H. I. Christensen, D. Kragic och F. Sandberg, "Vision for Interaction," i Dagstuhl Seminars, 2000, s. 51-73.
Icke refereegranskade
Artiklar
[370]
A. K. Engel et al., "Editorial : Sensorimotor Foundations of Social Cognition," Frontiers in Human Neuroscience, vol. 16, 2022.
[371]
A. L. Gert et al., "COORDINATING WITH A ROBOT PARTNER AFFECTS ACTION MONITORING RELATED NEURAL PROCESSING," Psychophysiology, vol. 58, s. S60-S60, 2021.
[372]
D. Kragic och Y. Sandamirskaya, "Effective and natural human-robot interaction requires multidisciplinary research," SCIENCE ROBOTICS, vol. 6, no. 58, 2021.
[373]
[374]
G.-Z. Yang, P. Dario och D. Kragic, "Social robotics-Trust, learning, and social interaction," Science Robotics, vol. 3, no. 21, 2018.
[375]
P. Fiorini och D. Kragic, "Education by competition," IEEE robotics & automation magazine, vol. 13, no. 3, s. 6-6, 2006.
[376]
D. Kragic och H. I. Christensen, "Advances in robot vision," Robotics and Autonomous Systems, vol. 52, no. 1, s. 1-3, 2005.
[377]
Konferensbidrag
[378]
R. Antonova, A. Rai och D. Kragic, "How to Sim2Real with Gaussian Processes: Prior Mean versus Kernels as Priors," i 2nd Workshop on Closing the Reality Gap in Sim2Real Transfer for Robotics. RSS, 2020. https://sim2real.github.io, 2020.
[379]
D. Almeida et al., "Team KTH’s Picking Solution for the Amazon Picking Challenge 2016," i Warehouse Picking Automation Workshop 2017 : Solutions, Experience, Learnings and Outlook of the Amazon Robotics Challenge, 2017.
[380]
P. Jensfelt, S. Ekvall och D. Kragic, "Integrating SLAM and Object Detection for Service Robot Tasks," i International Conference on Intelligent Robots and Systems, 2005.
[381]
D. Kragic et al., "Issues and Strategies for Robotic Object Manipulation in Domestic Settings," i (IROS'04), Workshop on Advances in Robot Vision – From Domestic Environments to. Medical Applications, Sendai, Japan, September 2004, 2004.
[382]
J. Lacroix et al., "Title of the deliverable : The Integration of Objects and Action Plans," i Workshop Farbbildverarbeitung - FWS 2004, 2004.
Kapitel i böcker
[383]
K. Hang et al., "Team CVAP’s Mobile Picking System at the Amazon Picking Challenge 2015," i Advances on Robotic Item Picking : Applications in Warehousing and E-Commerce Fulfillment, : Springer Nature, 2020, s. 1-12.
[384]
D. Almeida et al., "Team KTH’s Picking Solution for the Amazon Picking Challenge 2016," i Advances on Robotic Item Picking: Applications in Warehousing and E-Commerce Fulfillment, : Springer Nature, 2020, s. 53-62.
[385]
D. Kragic och K. Daniilidis, "3-D vision for navigation and grasping," i Springer Handbook of Robotics, : Springer International Publishing, 2016, s. 811-824.
[386]
D. Kragic och H. Christensen, "Robust Visual Servoing," i Household Service Robotics, : Elsevier, 2014, s. 397-427.
Avhandlingar
[387]
D. Kragic, "Visual Servoing for Manipulation : Robustness and Integration Issues," Doktorsavhandling Stockholm : KTH, Trita-NA, 0116, 2001.
Rapporter
[388]
M. Šarić, C. H. Ek och D. Kragić, "Dimensionality Reduction via Euclidean Distance Embeddings," Stockholm, Sweden : KTH Royal Institute of Technology, CAS/CVAP, TRITA-CSC-CV, 2011:2 CVAP320, 2011.
Proceedings (redaktörskap)
[389]
"Unifying Perspectives in Computational and Robot Vision," , Springer, Lecture Notes in Electrical Engineering, 8, 2008.
Övriga
[390]
J. Bütepage, H. Kjellström och D. Kragic, "A Probabilistic Semi-Supervised Approach to Multi-Task Human Activity Modeling," (Manuskript).
[391]
R. Antonova et al., "Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control," (Manuskript).
[392]
C. Chamzas et al., "Comparing Reconstruction- and Contrastive-based Models for Visual Task Planning," (Manuskript).
[393]
H. I. Christensen, D. Kragic och F. Sandberg, "Computational Vision for Interaction with People and Robots," (Manuskript).
[394]
[395]
M. Lippi et al., "Enabling Visual Action Planning for Object Manipulation through Latent Space Roadmap," (Manuskript).
[396]
P. Poklukar et al., "GraphDCA - a Framework for Node Distribution Comparison in Real and Synthetic Graphs," (Manuskript).
[397]
G. L. Marchetti et al., "Harmonics of Learning: Universal Fourier Features Emerge in Invariant Networks," (Manuskript).
[398]
S. Cruciani, H. Yin och D. Kragic, "In-Hand Manipulation of Objects with Unknown Shapes," (Manuskript).
[399]
S. Cruciani, Y. Hang och D. Kragic, "In-Hand Manipulation of Objects with Unknown Shapes," (Manuskript).
[400]
S. van Waveren et al., "Large-Scale Scenario Generation for Robotic Manipulation via Conditioned Generative Models," (Manuskript).
[401]
[402]
J. A. Haustein et al., "Learning Manipulation States and Actions for Efficient Non-prehensile Rearrangement Planning," (Manuskript).
[403]
S. Abdul Khader et al., "Learning Stable Normalizing-Flow Control for Robotic Manipulation," (Manuskript).
[404]
H. Yin, M. C. Welle och D. Kragic, "Policy Learning with Embedded Koopman Optimal Control," (Manuskript).
[405]
[406]
I. Mitsioni et al., "Safe Data-Driven Model Predictive Control of Systems with Complex Dynamics," (Manuskript).
[407]
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