5 citations · 6 across the 5 of their papers we have counts for
5 papers
Leveraging Foundation Models for Enhancing Robot Perception and Action
Reihaneh Mirjalili
This thesis investigates how foundation models can be systematically leveraged to enhance robotic capabilities, enabling more effective localization, interaction, and manipulation…
Augmented Reality for RObots (ARRO): Pointing Visuomotor Policies Towards Visual Robustness
Reihaneh Mirjalili, Tobias Jülg, Florian Walter +1
Visuomotor policies trained on human expert demonstrations have recently shown strong performance across a wide range of robotic manipulation tasks. However, these policies remain…
VLM-Vac: Enhancing Smart Vacuums through VLM Knowledge Distillation and Language-Guided Experience Replay
Reihaneh Mirjalili, Michael Krawez, Florian Walter +1
In this paper, we propose VLM-Vac, a novel framework designed to enhance the autonomy of smart robot vacuum cleaners. Our approach integrates the zero-shot object detection capabil…
Lan-grasp: Using Large Language Models for Semantic Object Grasping and Placement
Reihaneh Mirjalili, Michael Krawez, Yannik Blei +3
In this paper, we propose Lan-grasp, a novel approach towards more appropriate semantic grasping and placing. We leverage foundation models to equip the robot with a semantic under…
FM-Loc: Using Foundation Models for Improved Vision-based Localization
Reihaneh Mirjalili, Michael Krawez, Wolfram Burgard
Visual place recognition is essential for vision-based robot localization and SLAM. Despite the tremendous progress made in recent years, place recognition in changing environments…