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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.

Publikationer av författare från RPL

[1]
U. Wennberg and G. E. Henter, "The Case for Translation-Invariant Self-Attention in Transformer-Based Language Models," in ACL-IJCNLP 2021 : THE 59TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 11TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 2, 2021, pp. 130-140.
[2]
C. Ceylan, S. Franzen and F. T. Pokorny, "Learning Node Representations Using Stationary Flow Prediction on Large Payment and Cash Transaction Networks," in International Conference On Machine Learning, Vol 139, 2021.
[3]
N. Masud, "About Physical Human Robotic Interaction for Assistive Exoskeletons," Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2021:58, 2021.
[4]
H. Eivazi et al., "Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence," International Journal of Heat and Fluid Flow, vol. 90, 2021.
[5]
T. Kucherenko et al., "Speech2Properties2Gestures : Gesture-Property Prediction as a Tool for Generating Representational Gestures from Speech," in International Conference on Intelligent Virtual Agents, 2021.
[6]
S. van Waveren et al., "Exploring Non-Expert Robot Programming Through Crowdsourcing," Frontiers in Robotics and AI, vol. 8, 2021.
[7]
K. Grover et al., "Semantic Abstraction-Guided Motion Planning for scLTL Missions in Unknown Environments," in ROBOTICS : SCIENCE AND SYSTEM XVII, 2021.
[8]
A. Guemes et al., "From coarse wall measurements to turbulent velocity fields through deep learning," Physics of fluids, vol. 33, no. 7, 2021.
[9]
A. Czeszumski et al., "Coordinating With a Robot Partner Affects Neural Processing Related to Action Monitoring," Frontiers in Neurorobotics, vol. 15, 2021.
[10]
Ö. Özkahraman and P. Ögren, "Efficient Navigation Aware Seabed Coverage using AUVs," in Proceedings of 2021 IEEE International Conference on Safety, Security, and Rescue Robotics (SSRR), October 25-27 2021, New York, USA., 2021.
[11]
E. Morast and P. Jensfelt, "Towards Next Best View Planning for Time-Variant Scenes," in 2021 7th international conference on automation, robotics and applications (icara 2021), 2021, pp. 247-252.
[12]
R. Bonnevie, D. Duberg and P. Jensfelt, "Long-Term Exploration in Unknown Dynamic Environments," in 2021 7Th International Conference On Automation, Robotics And Applications (Icara 2021), 2021, pp. 32-37.
[13]
S. Abdul Khader, "Data-Driven Methods for Contact-Rich Manipulation: Control Stability and Data-Efficiency," Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 49, 2021.
[14]
F. I. Dogan, "Social Robots That Understand Natural Language Instructions and Resolve Ambiguities," in RSS Pioneers 2021 - Held in conjunction with the main Robotics: Science and Systems (RSS) Conference, 2021, 2021.
[16]
[17]
P. H. Andersen et al., "Towards Machine Recognition of Facial Expressions of Pain in Horses," Animals, vol. 11, no. 6, 2021.
[18]
[19]
A. Linard et al., "Formalizing Trajectories in Human-Robot Encounters via Probabilistic STL Inference," in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021.
[20]
I. Torre et al., "Should Robots Chicken? : How Anthropomorphism and Perceived Autonomy Influence Trajectories in a Game-Theoretic Problem," in Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, 2021, pp. 370-379.
[21]
F. Esposito et al., "Learning Task Constraints in Visual-Action Planning from Demonstrations," in IEEE Int. Conf. on Robot and Human Interactive Communication, 2021.
[22]
H. Yin, A. Varava and D. Kragic, "Modeling, learning, perception, and control methods for deformable object manipulation," Science Robotics, vol. 6, no. 54, 2021.
[23]
C. Pek, "Discrimination through algorithms and AI: Technical aspects," in Künstliche Intelligenz : Recht und Praxis automatisierter und autonomer Systeme, Kuuya Josef Chibanguza, Christian Kuß and Hans Steege Ed., : Nomos Verlagsgesellschaft, 2021.
[24]
T. Kucherenko et al., "A large, crowdsourced evaluation of gesture generation systems on common data : The GENEA Challenge 2020," in Proceedings IUI '21: 26th International Conference on Intelligent User Interfaces, 2021, pp. 11-21.
[25]
R. Gieselmann and F. T. Pokorny, "Planning-Augmented Hierarchical Reinforcement Learning," IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 5097-5104, 2021.
[26]
T. Nyberg et al., "Risk-aware Motion Planning for Autonomous Vehicles with Safety Specifications," in 32nd IEEE Intelligent Vehicles Symposium, July 11-17, 2021 Nagoya University, Nagoya, Japan [Virtual], 2021.
[27]
S. Bujwid and J. Sullivan, "Large-Scale Zero-Shot Image Classification from Rich and Diverse Textual Descriptions," in Proceedings of the Third Workshop on Beyond Vision and LANguage : inTEgrating Real-world kNowledge (LANTERN), 2021, pp. 38-52.
[28]
E. Heiden et al., "Bench-MR : A Motion Planning Benchmark for Wheeled Mobile Robots," IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 4536-4543, 2021.
[29]
M. Lee et al., "Robo-Identity: Exploring Artificial Identity and Multi-Embodiment," in ACM/IEEE International Conference on Human-Robot Interaction, 2021.
[30]
C. Pek and M. Althoff, "Fail-Safe Motion Planning for Online Verification of Autonomous Vehicles Using Convex Optimization," IEEE Transactions on robotics, vol. 37, no. 3, pp. 798-814, 2021.
[31]
E. Scukins and P. Ögren, "Using Reinforcement Learning to Create Control Barrier Functions for Explicit Risk Mitigation in Adversarial Environments," in IEEE International Conference on Robotics and Automation (ICRA), 30 May- 5 June 2021, 2021.
[32]
P. Tajvar, P.-J. Meyer and J. Tumova, "Closed-loop incremental stability for efficient symbolic control of non-linear systems," in 7th IFAC Conference on Analysis and Design of Hybrid Systems (ADHS), 2021.
[33]
L. Zhang et al., "Ankle Joint Torque Estimation Using an EMG-Driven Neuromusculoskeletal Model and an Artificial Neural Network Model," IEEE Transactions on Automation Science and Engineering, vol. 18, no. 2, pp. 564-573, 2021.
[34]
J. Karlsson et al., "Encoding Human Driving Styles in Motion Planning for Autonomous Vehicles," in ICRA International Conference on Robotics and Automation, 2021.
[35]
S. Gillet et al., "Robot Gaze Can Mediate Participation Imbalance in Groups with Different Skill Levels," in Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, 2021, pp. 303-311.
[36]
I. Torre, F. I. Dogan and D. Kontogiorgos, "Voice, Embodiment, and Autonomy as Identity Affordances," in HRI '21 Companion: Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, 2021.
[39]
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.
[40]
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.
[41]
J. Mänttäri, "Interpretable, Interaction-Aware Vehicle Trajectory Prediction with Uncertainty," Doctoral thesis : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2021:9, 2021.
[42]
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.
[43]
A. Varava et al., "Free Space of Rigid Objects : Caging, Path Non-existence, and Narrow Passage Detection," Springer Proceedings in Advanced Robotics, vol. 14, pp. 19-35, 2020.
[44]
L. Guastoni et al., "Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks," in Journal of Physics : Conference Series, 2020, p. 012022.
[45]
R. Gieselmann and F. T. Pokorny, "Standard deep generative models for density estimation in configuration spaces : A study of benefits, limits and challenges," in IEEE International Conference on Intelligent Robots and Systems, 2020, pp. 5238-5245.
[46]
C. Liang, R. A. Knepper and F. T. Pokorny, "No map, no problem : A local sensing approach for navigation in human-made spaces using signs," in IEEE International Conference on Intelligent Robots and Systems, 2020, pp. 6148-6155.
[47]
S. Krishnan et al., "SWIRL : A SequentialWindowed Inverse Reinforcement Learning Algorithm for Robot Tasks With Delayed Rewards," Springer Proceedings in Advanced Robotics, vol. 13, pp. 672-687, 2020.
[48]
C. Sprague, D. Izzo and P. Ögren, "Learning Dynamic-Objective Policies from a Class of Optimal Trajectories," in Proceedings of the IEEE Conference on Decision and Control, 2020, pp. 597-602.
[49]
J. Mahler et al., "Synthesis of Energy-Bounded Planar Caging Grasps using Persistent Homology," Springer Proceedings in Advanced Robotics, vol. 13, pp. 416-431, 2020.
[50]
Ö. Özkahraman and P. Ögren, "Combining Control Barrier Functions and Behavior Trees for Multi-Agent Underwater Coverage Missions," in Proceedings of the IEEE Conference on Decision and Control, 2020, pp. 5275-5282.
Full list in the KTH publications portal