Maximilian Diehl

Doktorand vid Mekatronik
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Visar 10 publikationer

2024

Generating and Transferring Priors for Causal Bayesian Network Parameter Estimation in Robotic Tasks*

Maximilian Diehl, Karinne Ramirez-Amaro
IEEE Robotics and Automation Letters. Vol. 9 (2), p. 1011 -1018
Artikel i vetenskaplig tidskrift
2024

Learning Robot Skills From Demonstration for Multi-Agent Planning

Maximilian Diehl, Isacco Zappa, Andrea Maria Zanchettin et al
IEEE International Conference on Automation Science and Engineering, p. 2348-2355
Paper i proceeding
2023

A causal-based approach to explain, predict and prevent failures in robotic tasks

Maximilian Diehl, Karinne Ramirez-Amaro
Robotics and Autonomous Systems. Vol. 162
Artikel i vetenskaplig tidskrift
2023

The Importance of Human Factors for Trusted Human-Robot Collaborations

Karinne Ramirez-Amaro, Ilaria Torre, Maximilian Diehl et al
ACM International Conference Proceeding Series, p. 502-503
Paper i proceeding
2022

Why Did I Fail? a Causal-Based Method to Find Explanations for Robot Failures

Maximilian Diehl, Karinne Ramirez-Amaro
IEEE Robotics and Automation Letters. Vol. 7 (4), p. 8925-8932
Artikel i vetenskaplig tidskrift
2021

Work in Progress - Automated Generation of Robotic Planning Domains from Observations

Maximilian Diehl, Karinne Ramirez-Amaro
18th International Conference on Ubiquitous Robots (UR), Organised session: 'Robots in the household: A review of task knowledge acquisition, planning, and execution'
Övrigt konferensbidrag
2021

Optimizing robot planning domains to reduce search time for long-horizon planning

Maximilian Diehl, Chris Paxton, Karinne Ramirez-Amaro
Övrigt konferensbidrag
2021

Automated Generation of Robotic Planning Domains from Observations

Maximilian Diehl, Chris Paxton, Karinne Ramirez-Amaro
IEEE International Conference on Intelligent Robots and Systems, p. 6732-6738
Paper i proceeding
2020

Augmented Reality interface to verify Robot Learning

Maximilian Diehl, Alexander Plopski, Hirokazu Kato et al
29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020, p. 378-383
Paper i proceeding

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Visar 1 forskningsprojekt

2020–2025

Learning & Understanding Human-Centered Robotic Manipulation Strategies

Karinne Ramirez-Amaro Mekatronik
Yiannis Karayiannidis Mekatronik
Maximilian Diehl Mekatronik
Jonas Sjöberg Mekatronik
Chalmers AI-forskningscentrum (CHAIR)

5 publikationer finns
Det kan finnas fler projekt där Maximilian Diehl medverkar, men du måste vara inloggad som anställd på Chalmers för att kunna se dem.