Publications

Below are the divisions's 50 latest publications according to the KTH library database DiVA.

Link to the full list for RPL in KTH's publication portal can be found at the bottom of this list. Researchers also maintain individual publication lists, see People in the menu on the main page.

Publications by RPL Authors

[1]
R. Antonova et al., "Bayesian optimization in variational latent spaces with dynamic compression," in Proceedings of Machine Learning Research : Volume 100: Proceedings of the 3rd Annual Conference on Robot Learning (CoRL), 2020, pp. 456-465.
[2]
M. Hwasser, D. Kragic and R. Antonova, "Variational Auto-Regularized Alignment for Sim-to-Real Control," in 2020 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 2020.
[3]
J. Bütepage et al., "Imitating by Generating : Deep Generative Models for Imitation of Interactive Tasks," Frontiers in Robotics and AI, vol. 7, 2020.
[4]
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, pp. 4321-4328, 2020.
[5]
H. Karaoǧuz and H. I. Bozma, "Merging of appearance-based place knowledge among multiple robots," Autonomous Robots, 2020.
[6]
[7]
A. Maki, "Special Section on Machine Vision and its Applications FOREWORD," IEICE transactions on information and systems, vol. E103D, no. 6, pp. 1208-1208, 2020.
[8]
I. Torroba et al., "PointNetKL : Deep Inference for GICP Covariance Estimation in Bathymetric SLAM," IEEE Robotics and Automation Letters, vol. 5, no. 3, pp. 4078-4085, 2020.
[10]
S. Gillet and I. Leite, "A Robot Mediated Music Mixing Activity for Promoting Collaboration among Children," in Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2020, 2020, pp. 212-214.
[11]
O. Kravchenko et al., "A Robotics-Inspired Screening Algorithm for Molecular Caging Prediction," Journal of Chemical Information and Modeling, vol. 60, no. 3, pp. 1302-1316, 2020.
[12]
J. Tang, "Deep Learning Assisted Visual Odometry," Doctoral thesis : KTH Royal Institute of Technology, 2020.
[13]
J. Tang et al., "Neural Outlier Rejection for Self-Supervised KeypointLearning," in International Conference on Learning Representations(ICLR), Apr 26th through May 1st, 2020, 2020.
[14]
M. Kokic, D. Kragic and J. Bohg, "Learning Task-Oriented Grasping From Human Activity Datasets," IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 3352-3359, 2020.
[16]
I. Garcia-Camacho et al., "Benchmarking Bimanual Cloth Manipulation," IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 1111-1118, 2020.
[17]
R. Vinuesa et al., "The role of artificial intelligence in achieving the Sustainable Development Goals," Nature Communications, vol. 11, no. 1, 2020.
[19]
Y. Xie, N. Bore and J. Folkesson, "Inferring Depth Contours from Sidescan Sonar using Convolutional Neural Nets," IET radar, sonar & navigation, vol. 14, no. 2, pp. 328-334, 2020.
[20]
J. F. Pinto Basto de Carvalho, "Topological Methods for Motion Prediction and Caging," Doctoral thesis : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2020:11, 2020.
[21]
S. Alexanderson et al., "Style-Controllable Speech-Driven Gesture Synthesis Using Normalising Flows," Computer graphics forum (Print), vol. 39, no. 2, pp. 487-496, 2020.
[22]
S. Cruciani et al., "Benchmarking In-Hand Manipulation," IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 588-595, 2020.
[24]
D. Kontogiorgos et al., "Embodiment Effects in Interactions with Failing Robots," in SIGCHI Conference on Human Factors in Computing Systems, CHI ’20, April 25–30, 2020, Honolulu, HI, USA, 2020.
[25]
D. Kontogiorgos et al., "Behavioural Responses to Robot Conversational Failures," in International Conference on Human Robot Interaction (HRI), HRI ’20, March 23–26, 2020, Cambridge, United Kingdom, 2020.
[26]
J. A. Haustein, "Robot Manipulation Planning Among Obstacles: Grasping, Placing and Rearranging," Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2020:6, 2020.
[27]
L. Palmieri et al., "Dispertio : Optimal Sampling For Safe Deterministic Motion Planning," IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 362-368, 2020.
[28]
D. Almeida and Y. Karayiannidis, "A Lyapunov-Based Approach to Exploit Asymmetries in Robotic Dual-Arm Task Resolution," in Proceedings of the IEEE Conference on Decision and Control, 2019, pp. 4252-4258.
[29]
F. I. Dogan, S. Kalkan and I. Leite, "Learning to Generate Unambiguous Spatial Referring Expressions for Real-World Environments," in IEEE International Conference on Intelligent Robots and Systems, 2019, pp. 4992-4999.
[30]
Y. Gao et al., "Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human-Robot Interaction," in IEEE International Conference on Intelligent Robots and Systems, 2019, pp. 305-312.
[31]
J. Haustein et al., "Object Placement Planning and optimization for Robot Manipulators," in IEEE International Conference on Intelligent Robots and Systems, 2019, pp. 7417-7424.
[32]
M. Kokic, D. Kragic and J. Bohg, "Learning to Estimate Pose and Shape of Hand-Held Objects from RGB Images," in IEEE International Conference on Intelligent Robots and Systems, 2019, pp. 3980-3987.
[33]
A. Varava et al., "Partial Caging : A Clearance-Based Definition and Deep Learning," in IEEE International Conference on Intelligent Robots and Systems, 2019, pp. 1533-1540.
[34]
I. Torroba, N. Bore and J. Folkesson, "Towards Autonomous Industrial-Scale Bathymetric Surveying," in IEEE International Conference on Intelligent Robots and Systems, 2019, pp. 6377-6382.
[35]
D. Almeida, E. Ataer-Cansizoglu and R. Corcodel, "Detection, tracking and 3d modeling of objects with sparse rgb-d slam and interactive perception," in IEEE-RAS International Conference on Humanoid Robots, 2019, pp. 297-304.
[36]
S. Cruciani et al., "Dual-Arm In-Hand Manipulation Using Visual Feedback," in IEEE-RAS International Conference on Humanoid Robots, 2019, pp. 387-394.
[37]
I. Mitsioni et al., "Data-driven model predictive control for the contact-rich task of food cutting," in IEEE-RAS International Conference on Humanoid Robots, 2019, pp. 244-250.
[38]
M. Gamba et al., "On the geometry of rectifier convolutional neural networks," in Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 2019, pp. 793-797.
[39]
J. Haustein et al., "Placing objects with prior in-hand manipulation using dexterous manipulation graphs," in IEEE-RAS International Conference on Humanoid Robots, 2019, pp. 453-460.
[40]
X. Chen et al., "Meta-Learning for Multi-objective Reinforcement Learning," in IEEE International Conference on Intelligent Robots and Systems, 2019, pp. 977-983.
[41]
I. Torroba, N. Bore and J. Folkesson, "Towards Autonomous Industrial-Scale Bathymetric Surveying," in IEEE International Conference on Intelligent Robots and Systems, 2019, pp. 6377-6382.
[42]
A. Hamalainen et al., "Affordance Learning for End-to-End Visuomotor Robot Control," in IEEE International Conference on Intelligent Robots and Systems, 2019, pp. 1781-1788.
[43]
M. Kokic, D. Kragic and J. Bohg, "Learning to Estimate Pose and Shape of Hand-Held Objects from RGB Images," in IEEE International Conference on Intelligent Robots and Systems, 2019, pp. 3980-3987.
[44]
J. Butepage, H. Kjellström and D. Kragic, "Predicting the what and how - A probabilistic semi-supervised approach to multi-task human activity modeling," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2019, pp. 2923-2926.
[45]
M. Gamba et al., "On the geometry of rectifier convolutional neural networks," in Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 2019, pp. 793-797.
[46]
V. Li and A. Maki, "Feature contraction : New convnet regularization in image classification," in British Machine Vision Conference 2018, BMVC 2018, 2019.
[48]
A. Linard, D. Bucur and M. Stoelinga, "Fault trees from data : Efficient learning with an evolutionary algorithm," in 5th International Symposium on Dependable Software Engineering: Theories, Tools, and Applications, SETTA 2019, 2019, pp. 19-37.
[49]
D. Feng et al., "Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector," in 2019 30TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV19), 2019, pp. 667-674.
[50]
V. Polianskii and F. T. Pokorny, "Voronoi boundary classification : A high-dimensional geometric approach via weighted monte carlo integration," in 36th International Conference on Machine Learning, ICML 2019, 2019, pp. 9024-9035.
Full list in the KTH publications portal
Page responsible:Web editors at EECS
Belongs to: Robotics, Perception and Learning
Last changed: Nov 20, 2019