Zobrazeno 1 - 10
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pro vyhledávání: '"Babuska"'
Planning methods struggle with computational intractability in solving task-level problems in large-scale environments. This work explores leveraging the commonsense knowledge encoded in LLMs to empower planning techniques to deal with these complex
Externí odkaz:
http://arxiv.org/abs/2409.04775
Sim2real, that is, the transfer of learned control policies from simulation to real world, is an area of growing interest in robotics due to its potential to efficiently handle complex tasks. The sim2real approach faces challenges due to mismatches b
Externí odkaz:
http://arxiv.org/abs/2407.04328
Autor:
Benders, Dennis, Köhler, Johannes, Niesten, Thijs, Babuška, Robert, Alonso-Mora, Javier, Ferranti, Laura
To efficiently deploy robotic systems in society, mobile robots need to autonomously and safely move through complex environments. Nonlinear model predictive control (MPC) methods provide a natural way to find a dynamically feasible trajectory throug
Externí odkaz:
http://arxiv.org/abs/2406.11506
Autor:
Abere, Michael J., Choi, Hyein, Van Bastian, Levi, Jauregui, Luis, Babuska, Tomas F., Rodriguez, Mark. A, DelRio, Frank W., Whetten, Shaun R., Kustas, Andrew B.
The additive manufacture of compositionally graded Al/Cu parts by laser engineered net shaping (LENS) is demonstrated. The use of a blue light build laser enabled deposition on a Cu substrate. The thermal gradient and rapid solidification inherent to
Externí odkaz:
http://arxiv.org/abs/2403.19861
As large language models (LLMs) permeate more and more applications, an assessment of their associated security risks becomes increasingly necessary. The potential for exploitation by malicious actors, ranging from disinformation to data breaches and
Externí odkaz:
http://arxiv.org/abs/2311.11415
Currently, truss tomato weighing and packaging require significant manual work. The main obstacle to automation lies in the difficulty of developing a reliable robotic grasping system for already harvested trusses. We propose a method to grasp trusse
Externí odkaz:
http://arxiv.org/abs/2309.17170
Publikováno v:
in IEEE Access, vol. 11, pp. 61481-61501, 2023
Many real-world systems can be described by mathematical models that are human-comprehensible, easy to analyze and help explain the system's behavior. Symbolic regression is a method that can automatically generate such models from data. Historically
Externí odkaz:
http://arxiv.org/abs/2302.00773
Autor:
Sedlar, Jiri, Stepanova, Karla, Skoviera, Radoslav, Behrens, Jan K., Tuna, Matus, Sejnova, Gabriela, Sivic, Josef, Babuska, Robert
Publikováno v:
IEEE Robotics and Automation Letters, vol. 8, no. 5, pp. 2788-2795, 2023
This paper introduces a dataset for training and evaluating methods for 6D pose estimation of hand-held tools in task demonstrations captured by a standard RGB camera. Despite the significant progress of 6D pose estimation methods, their performance
Externí odkaz:
http://arxiv.org/abs/2209.07976
Many real-world problems can be naturally described by mathematical formulas. The task of finding formulas from a set of observed inputs and outputs is called symbolic regression. Recently, neural networks have been applied to symbolic regression, am
Externí odkaz:
http://arxiv.org/abs/2205.15764
Publikováno v:
IEEE Access, Vol 12, Pp 126176-126187 (2024)
As large language models (LLMs) permeate more and more applications, an assessment of their associated security risks becomes increasingly necessary. The potential for exploitation by malicious actors, ranging from disinformation to data breaches and
Externí odkaz:
https://doaj.org/article/029e0f9f89194bc0ab0325b01591f02b
