2 citations · 2 across the 6 of their papers we have counts for
6 papers
LLM4SFC: Sequential Function Chart Generation via Large Language Models
Ofek Glick, Vladimir Tchuiev, Marah Ghoummaid +2
While Large Language Models (LLMs) are increasingly used for synthesizing textual PLC programming languages like Structured Text (ST) code, other IEC 61131-3 standard graphical lan…
MATCH: Task-Driven Code Evaluation through Contrastive Learning
Marah Ghoummaid, Vladimir Tchuiev, Ofek Glick +2
AI-based code generation is increasingly prevalent, with GitHub Copilot estimated to generate 46% of the code on GitHub. Accurately evaluating how well generated code aligns with d…
Gradient-Free Training of Quantized Neural Networks
Noa Cohen, Omkar Joglekar, Dotan Di Castro +3
Training neural networks requires significant computational resources and energy. Methods like mixed-precision and quantization-aware training reduce bit usage, yet they still depe…
Towards Natural Language-Driven Assembly Using Foundation Models
Omkar Joglekar, Tal Lancewicki, Shir Kozlovsky +3
Large Language Models (LLMs) and strong vision models have enabled rapid research and development in the field of Vision-Language-Action models that enable robotic control. The mai…
DUQIM-Net: Probabilistic Object Hierarchy Representation for Multi-View Manipulation
Vladimir Tchuiev, Yakov Miron, Dotan Di-Castro
Object manipulation in cluttered scenes is a difficult and important problem in robotics. To efficiently manipulate objects, it is crucial to understand their surroundings, especia…
InsertionNet 2.0: Minimal Contact Multi-Step Insertion Using Multimodal Multiview Sensory Input
Oren Spector, Vladimir Tchuiev, Dotan Di Castro
We address the problem of devising the means for a robot to rapidly and safely learn insertion skills with just a few human interventions and without hand-crafted rewards or demons…