Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Christian Pek"'
Publikováno v:
IEEE Transactions on Robotics. :1-17
In this paper, we address the safety and efficiency of data-driven model predictive controllers (DD-MPC) for systems with complex dynamics. First, we utilize safe exploration of dynamical systems to learn an accurate model for the DD-MPC. During trai
Publikováno v:
Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction.
Publikováno v:
IEEE Robotics and Automation Letters. 7:406-413
Despite the successes of deep reinforcement learning (RL), it is still challenging to obtain safe policies. Formal verifi- cation approaches ensure safety at all times, but usually overly restrict the agent’s behaviors, since they assume adversaria
Many tasks require robots to manipulate objects while satisfying a complex interplay of spatial and temporal constraints. For instance, a table setting robot first needs to place a mug and then fill it with coffee, while satisfying spatial relations
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::23ce0f0eb420b83e455a52e3d2138624
https://doi.org/10.21203/rs.3.rs-2430844/v1
https://doi.org/10.21203/rs.3.rs-2430844/v1
Autor:
Christian Pek, Matthias Althoff
Publikováno v:
IEEE Transactions on Robotics. 37:798-814
Safe motion planning for autonomous vehicles is a challenging task, since the exact future motion of other traffic participant is usually unknown. In this article, we present a verification technique ensuring that autonomous vehicles do not cause col
Publikováno v:
Nature Machine Intelligence. 2:518-528
Ensuring that autonomous vehicles do not cause accidents remains a challenge. We present a formal verification technique for guaranteeing legal safety in arbitrary urban traffic situations. Legal safety means that autonomous vehicles never cause acci
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031223365
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f962ca2fb60383b1fc501f0e2d516e5a
https://doi.org/10.1007/978-3-031-22337-2_31
https://doi.org/10.1007/978-3-031-22337-2_31
Publikováno v:
ICRA
Driving styles play a major role in the acceptance and use of autonomous vehicles. Yet, existing motion planning techniques can often only incorporate simple driving styles that are modeled by the developers of the planner and not tailored to the pas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c75f0e3cdc15a01a1fbf38bb6ccc1258
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-310389
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-310389
Publikováno v:
IV
Ensuring the safety of autonomous vehicles (AVs) in uncertain traffic scenarios is a major challenge. In this paper, we address the problem of computing the risk that AVs violate a given safety specification in uncertain traffic scenarios, where stat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::74403a14d8d610af67a7bcd36488df17
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296090
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296090
Publikováno v:
RO-MAN
Visual planning approaches have shown great success for decision making tasks with no explicit model of the state space. Learning a suitable representation and constructing a latent space where planning can be performed allows non-experts to setup an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b25c56cf7b7909bd3dcabe3bd8e601da
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-305406
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-305406