Baris Can Yalçin — accepted AI-conference papers

2025

MPC-based Deep Reinforcement Learning Method for Space Robotic Control with Fuel Sloshing Mitigation

Mahya Ramezani, M. Amin Alandihallaj, Baris Can Yalçin, Miguel Angel Olivares-Méndez, Holger Voos

IROS 2025

This paper presents an integrated Reinforcement Learning (RL) and Model Predictive Control (MPC) framework for autonomous satellite docking with a partially filled fuel tank. Traditional docking control faces challenges due to fuel sloshing in microgravity, which induces unpredictable forces affecti

Cited by 3 Source ↗Scholar ↗

2022

Enhancing Rover Teleoperation on the Moon With Proprioceptive Sensors and Machine Learning Techniques

Sofía Coloma, Carol Martínez, Baris Can Yalçin, Miguel A. Olivares-Méndez

RA-L 2022

Geological formations, environmental conditions, and soil mechanics frequently generate undesired effects on rovers' mobility, such as slippage or sinkage. Underestimating these undesired effects may compromise the rovers' operation and lead to a premature end of the mission. Minimizing mobility ris

Cited by 16 Source ↗Scholar ↗