activity
20192026
most citedL3MVN: Leveraging Large Language Models for Visual Target Navigation

100 citations · 150 across the 34 of their papers we have counts for

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Showing 2022Show all

8 papers · 1 filter

cs.RO2022

Throwing Objects into A Moving Basket While Avoiding Obstacles

Hamidreza Kasaei, Mohammadreza Kasaei

The capabilities of a robot will be increased significantly by exploiting throwing behavior. In particular, throwing will enable robots to rapidly place the object into the target…

cs.CV2022★ 2 cited

Enhancing Fine-Grained 3D Object Recognition using Hybrid Multi-Modal Vision Transformer-CNN Models

Songsong Xiong, Georgios Tziafas, Hamidreza Kasaei

Robots operating in human-centered environments, such as retail stores, restaurants, and households, are often required to distinguish between similar objects in different contexts…

cs.RO2022

GraspCaps: A Capsule Network Approach for Familiar 6DoF Object Grasping

Tomas van der Velde, Hamed Ayoobi, Hamidreza Kasaei

As robots become more widely available outside industrial settings, the need for reliable object grasping and manipulation is increasing. In such environments, robots must be able…

cs.RO2022★ 2 cited

Enhancing Interpretability and Interactivity in Robot Manipulation: A Neurosymbolic Approach

Georgios Tziafas, Hamidreza Kasaei

In this paper we present a neurosymbolic architecture for coupling language-guided visual reasoning with robot manipulation. A non-expert human user can prompt the robot using unco…

cs.CV2022★ 1 cited

Early or Late Fusion Matters: Efficient RGB-D Fusion in Vision Transformers for 3D Object Recognition

Georgios Tziafas, Hamidreza Kasaei

The Vision Transformer (ViT) architecture has established its place in computer vision literature, however, training ViTs for RGB-D object recognition remains an understudied topic…

cs.RO2022★ 3 cited

IPPO: Obstacle Avoidance for Robotic Manipulators in Joint Space via Improved Proximal Policy Optimization

Yongliang Wang, Hamidreza Kasaei

Reaching tasks with random targets and obstacles is a challenging task for robotic manipulators. In this study, we propose a novel model-free reinforcement learning approach based…