19 citations · 23 across the 14 of their papers we have counts for
16 papers
Multimodal MPC via Bayesian Set Optimization for Dynamic Manipulation of Deformable Linear Objects
Youyuan Long, Gokhan Solak, Arash Ajoudani
Dynamic manipulation of deformable linear objects (DLOs) remains challenging due to underactuated, high-dimensional dynamics, delayed deformation responses, and the presence of mul…
Graph-Based Recognition of Simulated Train-Driver States From Facial and Upper-Body Keypoints
Olivia Nocentini, Marta Lagomarsino, Gokhan Solak +6
Driver fatigue poses a significant challenge to railway safety, with traditional systems like the dead-man switch offering limited and basic alertness checks. This study presents a…
Potential-Field Action Representation for Reinforcement Learning in Contact-Rich Manipulation
Xinyu Liu, Gökhan Solak, Arash Ajoudani
Model-free reinforcement learning can acquire contact-rich robotic manipulation skills through trial-and-error interaction, but it often requires the policy to learn both task stra…
FORTE: Task-Adaptive Force Capability Optimization for Mobile Manipulators
Xiao Wang, Heng Zhang, Gokhan Solak +2
Effective physical interaction control in robotic manipulation requires not only kinematically feasible motion but also sufficient force-interaction capability. Existing redundancy…
Dissipative Latent Residual Physics-Informed Neural Networks for Modeling and Identification of Electromechanical Systems
Youyuan Long, Gokhan Solak, Arash Ajoudani
Accurate dynamical modeling is essential for simulation and control of embodied systems, yet first-principles models of electromechanical systems often fail to capture complex diss…
Self-supervised Physics-Informed Manipulation of Deformable Linear Objects with Non-negligible Dynamics
Youyuan Long, Gokhan Solak, Sara Zeynalpour +2
We address dynamic manipulation of deformable linear objects by presenting SPiD, a physics-informed self-supervised learning framework that couples an accurate deformable object mo…
Safe Learning for Contact-Rich Robot Tasks: A Survey from Classical Learning-Based Methods to Safe Foundation Models
Heng Zhang, Rui Dai, Gokhan Solak +4
Contact-rich tasks pose significant challenges for robotic systems due to inherent uncertainty, complex dynamics, and the high risk of damage during interaction. Recent advances in…
CompliantVLA-adaptor: VLM-Guided Variable Impedance Action for Safe Contact-Rich Manipulation
Heng Zhang, Wei-Hsing Huang, Qiyi Tong +7
We propose a CompliantVLA-adaptor that augments the state-of-the-art Vision-Language-Action (VLA) models with vision-language model (VLM)-informed context-aware variable impedance…
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…
SRL-VIC: A Variable Stiffness-Based Safe Reinforcement Learning for Contact-Rich Robotic Tasks
Heng Zhang, Gokhan Solak, Gustavo J. G. Lahr +1
Reinforcement learning (RL) has emerged as a promising paradigm in complex and continuous robotic tasks, however, safe exploration has been one of the main challenges, especially i…