1 citations · 1 across the 4 of their papers we have counts for
7 papers
TalkWithMachines: Enhancing Human-Robot Interaction for Interpretable Industrial Robotics Through Large/Vision Language Models
Ammar N. Abbas, Csaba Beleznai
TalkWithMachines aims to enhance human-robot interaction by contributing to interpretable industrial robotic systems, especially for safety-critical applications. The presented pap…
Safety-Driven Deep Reinforcement Learning Framework for Cobots: A Sim2Real Approach
Ammar N. Abbas, Shakra Mehak, Georgios C. Chasparis +4
This study presents a novel methodology incorporating safety constraints into a robotic simulation during the training of deep reinforcement learning (DRL). The framework integrate…
BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data
Mateusz Łajszczak, Guillermo Cámbara, Yang Li +16
We introduce a text-to-speech (TTS) model called BASE TTS, which stands for ig daptive treamable TTS with mergent abilities. BASE TT…
Hierarchical Framework for Interpretable and Probabilistic Model-Based Safe Reinforcement Learning
Ammar N. Abbas, Georgios C. Chasparis, John D. Kelleher
The difficulty of identifying the physical model of complex systems has led to exploring methods that do not rely on such complex modeling of the systems. Deep reinforcement learni…
Specialized Deep Residual Policy Safe Reinforcement Learning-Based Controller for Complex and Continuous State-Action Spaces
Ammar N. Abbas, Georgios C. Chasparis, John D. Kelleher
Traditional controllers have limitations as they rely on prior knowledge about the physics of the problem, require modeling of dynamics, and struggle to adapt to abnormal situation…
Simple and Effective Multi-sentence TTS with Expressive and Coherent Prosody
Peter Makarov, Ammar Abbas, Mateusz Łajszczak +5
Generating expressive and contextually appropriate prosody remains a challenge for modern text-to-speech (TTS) systems. This is particularly evident for long, multi-sentence inputs…