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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]
T. Kucherenko et al., "Moving Fast and Slow : Analysis of Representations and Post-Processing in Speech-Driven Automatic Gesture Generation," International Journal of Human-Computer Interaction, pp. 1-17, 2021.
[2]
A. Ghadirzadeh et al., "Human-Centered Collaborative Robots With Deep Reinforcement Learning," IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 566-571, 2021.
[3]
J. Mänttäri, "Interpretable, Interaction-Aware Vehicle Trajectory Prediction with Uncertainty," Doctoral thesis : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2021.
[4]
S. A. Khader et al., "Stability-Guaranteed Reinforcement Learning for Contact-Rich Manipulation," IEEE Robotics and Automation Letters, vol. 6, no. 1, pp. 1-8, 2021.
[5]
P. Jonell et al., "Can we trust online crowdworkers? : Comparing online and offline participants in a preference test of virtual agents.," in IVA '20: Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents, 2020.
[6]
P. Jonell et al., "Let’s face it : Probabilistic multi-modal interlocutor-aware generation of facial gestures in dyadic settings," in IVA '20: Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents, 2020.
[7]
J. Karlsson and J. Tumova, "Intention-aware motion planning with road rules," in IEEE International Conference on Automation Science and Engineering, 2020, pp. 526-532.
[8]
P. Tajvar, F. S. Barbosa and J. Tumova, "Safe Motion Planning for an Uncertain Non-Holonomic System with Temporal Logic Specification," in IEEE International Conference on Automation Science and Engineering, 2020, pp. 349-354.
[9]
M. Rashid, H. Kjellström and Y. J. Lee, "Action Graphs : Weakly-supervised Action Localization with Graph Convolution Networks," in 2020 ieee winter conference on applications of computer vision (wacv), 2020, pp. 604-613.
[10]
[11]
C. Licoppe and S. Tuncer, "Analyzing interactions at work : The case of electronic monitoring," Sociologie du travail, vol. 62, no. 1-2, 2020.
[12]
F. I. Dogan et al., "The impact of adding perspective-taking to spatial referencing during human-robot interaction," Robotics and Autonomous Systems, vol. 134, 2020.
[13]
V. Polianskii and F. T. Pokorny, "Voronoi Graph Traversal in High Dimensions with Applications to Topological Data Analysis and Piecewise Linear Interpolation," in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2020, pp. 2154-2164.
[14]
F. Yang et al., "Group Behavior Recognition Using Attention- and Graph-Based Neural Networks," in the 24th European Conference on Artificial Intelligence (ECAI), 29 aug -8 sep, 2020, 2020.
[15]
S. Tuncer, O. Lindwall and B. Brown, "Making Time : Pausing to Coordinate Video Instructions and Practical Tasks," Symbolic interaction, 2020.
[16]
X. Zhang et al., "How Do Fair Decisions Fare in Long-term Qualification?," in Proceedings of the NeurIPS 2020, 2020.
[17]
A. Varava et al., "Free space of rigid objects : caging, path non-existence, and narrow passage detection," The international journal of robotics research, 2020.
[19]
T. Kucherenko et al., "Gesticulator : A framework for semantically-aware speech-driven gesture generation," in ICMI '20: Proceedings of the 2020 International Conference on Multimodal Interaction, 2020.
[20]
D. Duberg and P. Jensfelt, "UFOMap : An Efficient Probabilistic 3D Mapping Framework That Embraces the Unknown," IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 6411-6418, 2020.
[21]
X. Chen, "Data-Efficient Reinforcement and Transfer Learning in Robotics," Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2020:64, 2020.
[22]
X. Chen et al., "Adversarial Feature Training for Generalizable Robotic Visuomotor Control," in Proceedings - IEEE International Conference on Robotics and Automation, 2020.
[23]
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.
[24]
S. Alexanderson et al., "Style-Controllable Speech-Driven Gesture Synthesis Using Normalising FlowsKeywords," Computer graphics forum (Print), vol. 39, no. 2, pp. 487-496, 2020.
[26]
S. Gillet, W. van den Bos and I. Leite, "A social robot mediator to foster collaboration and inclusion among children," in Robotics : Science and systems XVI, 2020.
[27]
R. Antonova, "Transfer-Aware Kernels, Priors and Latent Spaces from Simulation to Real Robots," Doctoral thesis Stockholm, Sweden : KTH Royal Institute of Technology, TRITA-EECS-AVL, 54, 2020.
[28]
G. F. Schuppe and J. Tumova, "Multi-Agent Strategy Synthesis for LTL Specifications through Assumption Composition," in 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), 2020.
[29]
I. Torre, A. B. Latupeirissa and C. McGinn, "How context shapes the appropriateness of a robot’s voice," in 29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020, 2020, pp. 215-222.
[30]
I. Torre and S. Le Maguer, "Should robots have accents?," in 29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020August 2020, 2020, pp. 208-214.
[31]
N. Bore and J. Folkesson, "Modeling and Simulation of Sidescan Using Conditional Generative Adversarial Network," IEEE Journal of Oceanic Engineering, pp. 1-11, 2020.
[32]
J. Folkesson, H. Chang and N. Bore, "Lambert’s Cosine Law and Sidescan Sonar Modeling," in 2020 IEEE OES Autonomous Underwater Vehicle Symposium, 2020.
[33]
M. Larsson, N. Bore and J. Folkesson, "Latent Space Metric Learning For Sidescan Sonar Place Recognition," in 2020 IEEE OES Autonomous Underwater Vehicle Symposium, 2020.
[35]
B. Su, C. Smith and E. Gutierrez Farewik, "Gait Phase Recognition Using Deep Convolutional Neural Network with Inertial Measurement Units," Biosensors, vol. 10, no. 9, 2020.
[36]
R. Tu et al., "How Do Fair Decisions Fare in Long-term Qualification?," in Thirty-fourth Conference on Neural Information Processing Systems, 2020.
[37]
M. Lippi et al., "Latent Space Roadmap for Visual Action Planning of Deformable and Rigid Object Manipulation," in International Conference of Intelligent Robots and Systems 2020, 2020.
[38]
M. Kokic, "Learning for Task-Oriented Grasping," Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2020:48, 2020.
[39]
T. Ziegler et al., "Fashion Landmark Detection and Category Classification for Robotics," in IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), 2020.
[40]
F. Baldassarre et al., "Explanation-based Weakly-supervised Learning of Visual Relations with Graph Networks," in European Conference on Computer Vision, ECCV 2020, 2020.
[41]
S. Bhat et al., "A Cyber-Physical System for Hydrobatic AUVs: System Integration and Field Demonstration," in IEEE OES Autonomous Underwater Vehicles Symposium, St. Johns, Newfoundland, Canada, 2020, 2020.
[42]
Y. Zhong and A. Maki, "Regularizing CNN Transfer Learning with Randomised Regression," in IEEE Conference Computer Vision and Pattern Recognition 2020, 2020.
[43]
Ö. Özkahraman and P. Ögren, "Combining Control Barrier Functions and Behavior Trees for Multi-Agent Underwater Coverage Missions," in Proceedings of 59th Conference on Decision and Control, 2020, 2020.
[44]
Ö. Özkahraman and P. Ögren, "Underwater Caging and Capture for Autonomous Underwater Vehicles," in Global OCEANS 2020, 2020.
[45]
I. Arnekvist, "Transfer Learning using low-dimensional Representations in Reinforcement Learning," Licentiate thesis : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2020:39, 2020.
[47]
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.
[48]
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.
[49]
J. Bütepage et al., "Imitating by Generating : Deep Generative Models for Imitation of Interactive Tasks," Frontiers in Robotics and AI, vol. 7, 2020.
[50]
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.
Full list in the KTH publications portal
Page responsible:Web editors at EECS
Belongs to: Robotics, Perception and Learning
Last changed: Nov 20, 2019