5 citations · 15 across the 13 of their papers we have counts for
13 papers
Higher Resolution, Better Generalization: Unlocking Visual Scaling in Deep Reinforcement Learning
Raphael Trumpp, Ömer Veysel Çağatan, Barış Akgün +1
Pixel-based deep reinforcement learning agents are typically trained on heavily downsampled visual observations, a convention inherited from early benchmarks rather than grounded i…
Model-Based Adaptive Precision Control for Tabletop Planar Pushing Under Uncertain Dynamics
Aydin Ahmadi, Baris Akgun
Data-driven planar pushing methods have recently gained attention as they reduce manual engineering effort and improve generalization compared to analytical approaches. However, mo…
Failure Modes of Maximum Entropy RLHF
Ömer Veysel Çağatan, Barış Akgün
In this paper, we show that Simple Preference Optimization (SimPO) can be derived as Maximum Entropy Reinforcement Learning, providing a theoretical foundation for this reference-f…
Uncovering RL Integration in SSL Loss: Objective-Specific Implications for Data-Efficient RL
Ömer Veysel Çağatan, Barış Akgün
In this study, we investigate the effect of SSL objective modifications within the SPR framework, focusing on specific adjustments such as terminal state masking and prioritized re…
Privileged to Predicted: Towards Sensorimotor Reinforcement Learning for Urban Driving
Ege Onat Özsüer, Barış Akgün, Fatma Güney
Reinforcement Learning (RL) has the potential to surpass human performance in driving without needing any expert supervision. Despite its promise, the state-of-the-art in sensorimo…
BarlowRL: Barlow Twins for Data-Efficient Reinforcement Learning
Omer Veysel Cagatan, Baris Akgun
This paper introduces BarlowRL, a data-efficient reinforcement learning agent that combines the Barlow Twins self-supervised learning framework with DER (Data-Efficient Rainbow) al…
Keyframe Demonstration Seeded and Bayesian Optimized Policy Search
Onur Berk Tore, Farzin Negahbani, Baris Akgun
This paper introduces a novel Learning from Demonstration framework to learn robotic skills with keyframe demonstrations using a Dynamic Bayesian Network (DBN) and a Bayesian Optim…
Learning Markerless Robot-Depth Camera Calibration and End-Effector Pose Estimation
Bugra C. Sefercik, Baris Akgun
Traditional approaches to extrinsic calibration use fiducial markers and learning-based approaches rely heavily on simulation data. In this work, we present a learning-based marker…
FLAGS Framework for Comparative Analysis of Federated Learning Algorithms
Ahnaf Hannan Lodhi, Barış Akgün, Öznur Özkasap
Federated Learning (FL) has become a key choice for distributed machine learning. Initially focused on centralized aggregation, recent works in FL have emphasized greater decentral…
Frustum Fusion: Pseudo-LiDAR and LiDAR Fusion for 3D Detection
Farzin Negahbani, Onur Berk Töre, Fatma Güney +1
Most autonomous vehicles are equipped with LiDAR sensors and stereo cameras. The former is very accurate but generates sparse data, whereas the latter is dense, has rich texture an…
State-of-the-art Techniques in Deep Edge Intelligence
Ahnaf Hannan Lodhi, Barış Akgün, Öznur Özkasap
The potential held by the gargantuan volumes of data being generated across networks worldwide has been truly unlocked by machine learning techniques and more recently Deep Learnin…
Mind in the Machine: Perceived Minds Induce Decision Change
Deniz Lefkeli, Baris Akgun, Sahibzada Omar +3
Recent research on human robot interaction explored whether people's tendency to conform to others extends to artificial agents (Hertz & Wiese, 2016). However, little is known abou…
Flow From Motion: A Deep Learning Approach
Cem Eteke, Hayati Havlucu, Nisa İrem Kırbaç +5
Wearable devices have the potential to enhance sports performance, yet they are not fulfilling this promise. Our previous studies with 6 professional tennis coaches and 20 players…