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Robert Gieselmann
I am an AI Researcher at Amazon in Berlin. My work focuses on developing
fast, verifiable, and self-improving reasoning agents, primarily leveraging Large Language Models (LLMs).
Previously, I completed a PhD in Computer Science at KTH Royal Institute of Technology in Stockholm,
supervised by Florian T.
Pokorny, and supported by WASP, the Wallenberg AI, Autonomous Systems and Software Program. I completed several research internships, including at Meta and Bosch AI.
Before my PhD, I worked as a Research Assistant within machine learning and robotics at the Technical
University of Hamburg (TUHH). I received my M.Sc. in Robotics, Cognition, Intelligence from the
Technical University of Munich (TUM).
LinkedIn /
Google Scholar /
Github
Research
Fast-dRRT*: Efficient Multi-Robot Motion Planning for Automated Industrial
Manufacturing
Andrey Solano, Arne Sieverling, Robert Gieselmann, Andreas Orthey
Arxiv , 2024
[Paper]
Expansive Latent Planning for Sparse Reward Offline Reinforcement
Learning
Robert Gieselmann, Florian T. Pokorny
Conference on Robot Learning (CORL) , 2023 (oral
presentation 6.6%)
Previously RSS 2023 - Workshop on Learning for Task and Motion Planning (spotlight)
[Paper]
Latent Planning via Expansive Space Trees
Robert Gieselmann, Florian T. Pokorny
Neural Information Processing Systems (NeurIPS) , 2022 (acceptance rate 25.6%)
[Paper]
DLO@Scale - A Large-Scale Meta Dataset for Learning Non-Rigid Object
Pushing Dynamics
Robert Gieselmann, Alberta Longhini, Alfredo Reichlin, Danica Kragic, Florian T.
Pokorny
[Paper][Website]
Workshop on Physical Reasoning and Inductive Biases for the Real World, NeurIPS , 2021
Planning-Augmented Hierarchical Reinforcement Learning
Robert Gieselmann, Florian T. Pokorny
IEEE Robotics and Automation Letters (RA-L), 2021
[Paper]
ReForm: A Robot Learning Sandbox for Deformable Linear Object
Manipulation
Rita Laezza*, Robert Gieselmann*, Florian T. Pokorny, Yiannis Karayiannidis
IEEE International Conference on Robotics and Automation (ICRA) , 2021
[Paper]
Standard Deep Generative Models for Density Estimation in Configuration
Spaces: A Study of Benefits, Limits and Challenges
Robert Gieselmann, Florian T. Pokorny
IEEE International Conference on Intelligent Robots and Systems (IROS) , 2020
[Paper]
Experience-Based Heuristic Search: Robust Motion Planning with Deep
Q-Learning
Julian Bernhard, Robert Gieselmann, Klemens Esterle, Alois Knoll
IEEE International Conference on Intelligent Transportation Systems (ITSC) , 2018
[Paper]
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