19 citations · 23 across the 14 of their papers we have counts for
7 papers · 1 filter
OmniVIC: A Self-Improving Variable Impedance Controller with Vision-Language In-Context Learning for Safe Robotic Manipulation
Heng Zhang, Wei-Hsing Huang, Gokhan Solak +1
We present OmniVIC, a universal variable impedance controller (VIC) enhanced by a vision language model (VLM), which improves safety and adaptation in any contact-rich robotic mani…
Anticipatory Fall Detection in Humans with Hybrid Directed Graph Neural Networks and Long Short-Term Memory
Younggeol Cho, Gokhan Solak, Olivia Nocentini +3
Detecting and preventing falls in humans is a critical component of assistive robotic systems. While significant progress has been made in detecting falls, the prediction of falls…
A Survey on Imitation Learning for Contact-Rich Tasks in Robotics
Toshiaki Tsuji, Yasuhiro Kato, Gokhan Solak +4
This paper comprehensively surveys research trends in imitation learning for contact-rich robotic tasks. Contact-rich tasks, which require complex physical interactions with the en…
Context-aware collaborative pushing of heavy objects using skeleton-based intention prediction
Gokhan Solak, Gustavo J. G. Lahr, Idil Ozdamar +1
In physical human-robot interaction, force feedback has been the most common sensing modality to convey the human intention to the robot. It is widely used in admittance control to…
A Framework for Adaptive Load Redistribution in Human-Exoskeleton-Cobot Systems
Emir Mobedi, Gokhan Solak, Arash Ajoudani
Wearable devices like exoskeletons are designed to reduce excessive loads on specific joints of the body. Specifically, single- or two-degrees-of-freedom (DOF) upper-body industria…
Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks
Heng Zhang, Gokhan Solak, Arash Ajoudani
Ensuring safety in reinforcement learning (RL)-based robotic systems is a critical challenge, especially in contact-rich tasks within unstructured environments. While the state-of-…
Passivity-Centric Safe Reinforcement Learning for Contact-Rich Robotic Tasks
Heng Zhang, Gokhan Solak, Sebastian Hjorth +1
Reinforcement learning (RL) has achieved remarkable success in various robotic tasks; however, its deployment in real-world scenarios, particularly in contact-rich environments, of…