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Demo Data from SIGIR 2026 / reviewers in the wild / expert

Pelle Gustafsson

dblp:334/3165 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Motion planning and robot control · 88% Robot manipulation · 12%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
robot control		 1.012026
End-Effector Cartesian Velocity Control for Redundant Loader Cranes Using Reinforcement Learning (Abstract Reprint) · AAAI 2026
Robotics › Motion planning and robot control
robot learning		 0.912025
End-Effector Cartesian Velocity Control for Redundant Loader Cranes Using Reinforcement learning · IEEE Trans. Robotics 2025
Robotics › Motion planning and robot control › manipulator control
end-effector control		 0.312025
End-Effector Cartesian Velocity Control for Redundant Loader Cranes Using Reinforcement learning · IEEE Trans. Robotics 2025

Methods — techniques the papers use, named apart from their topics

reinforcement learning · 1.9monte carlo evaluation · 1.9forward kinematics · 1.9
YearPublicationVenuePosition
2026 End-Effector Cartesian Velocity Control for Redundant Loader Cranes Using Reinforcement Learning (Abstract Reprint)
abstract
Loader cranes with multiple actuated joints are complex systems to be operated by humans. Development of advanced assistance functions, such as end-effector velocity control in Cartesian space allows for utilizing the machine to its full speed and potential, wherein actuator limits, load balance, and singularities, as well as other complicated effects are handled by the automated function. To this end, this article provides a reinforcement learning-based policy optimization workflow for training and evaluating controllers using large-scale, parallelized invocations of forward kinematics. Monte Carlo evaluations of the closed-loop model are performed to inspect the stability and performance in the whole operational envelope of the loader crane for safe deployment on real machines. Our approach does not require any explicit inverse-kinematics model and is free from complex or hard-coded actuator limits or objectives. Results of simulations and experiments on a real loader crane are provided to showcase the performance of our approach in comparison to Jacobian inverse-based methods.
Abdolreza Taheri, Amy Rankka, Pelle Gustafsson, Joni Pajarinen, Reza Ghabcheloo
 AAAI3
2025 End-Effector Cartesian Velocity Control for Redundant Loader Cranes Using Reinforcement learning
abstract
Loader cranes with multiple actuated joints are complex systems to be operated by humans. Development of advanced assistance functions, such as end-effector velocity control in Cartesian space allows for utilizing the machine to its full speed and potential, wherein actuator limits, load balance, and singularities, as well as other complicated effects are handled by the automated function. To this end, this article provides a reinforcement learning-based policy optimization workflow for training and evaluating controllers using large-scale, parallelized invocations of forward kinematics. Monte Carlo evaluations of the closed-loop model are performed to inspect the stability and performance in the whole operational envelope of the loader crane for safe deployment on real machines. Our approach does not require any explicit inverse-kinematics model and is free from complex or hard-coded actuator limits or objectives. Results of simulations and experiments on a real loader crane are provided to showcase the performance of our approach in comparison to Jacobian inverse-based methods.
Abdolreza Taheri, Amy Rankka, Pelle Gustafsson, Joni Pajarinen, Reza Ghabcheloo
 IEEE Trans. Robotics3