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Dr Peter Bohm

Postdoctoral Research Fellow
School of Electrical Engineering and Computer Science
p.bohm@uq.edu.au
View researcher profile

Publications

Conference Papers (5)
Thesis (1)

Conference Papers

Böhm, Peter, Pounds, Pauline and Chapman, Archie C. (2023). Feature extraction for effective and efficient deep reinforcement learning on real robotic platforms. IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, 29 May - 2 June 2023. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/icra48891.2023.10160862
Böhm, Peter, Chapman, Archie and Pounds, Pauline (2022). Training directional locomotion for quadrupedal low-cost robotic systems via deep reinforcement learning. Australasian Conference on Robotics and Automation (ACRA 2022) , Brisbane, QLD Australia, 6-8 December 2022. Australasian Robotics and Automation Association.
Böhm, Peter, Pounds, Pauline and Chapman, Archie C. (2022). Low-cost real-world implementation of the swing-up pendulum for deep reinforcement learning experiments. Australasian Conference on Robotics and Automation (ACRA 2022), Brisbane, QLD Australia, 6-8 December 2022. Australasian Robotics and Automation Association.
Bohm, Peter, Pounds, Pauline and Chapman, Archie C. (2022). Non-blocking asynchronous training for reinforcement learning in real-world environments. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 23-27 October 2022. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/iros47612.2022.9981333
Böhm, Peter and Singh, Surya P. N. (2019). A parallelized & asynchronous forward simulation framework to inform multi-robot UAV coordination via Akka streams. Australasian Conference on Robotics and Automation ACRA 2019, Adelaide, SA Australia, 9-11 December 2019. Adelaide, SA Australia: Australasian Robotics and Automation Association.

Thesis

Bohm, Peter (2024). Training robust deep reinforcement learning policies in the real world. PhD Thesis, School of Electrical Engineering and Computer Science, The University of Queensland. doi: 10.14264/cf1a3b1
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