19 citations · 23 across the 14 of their papers we have counts for
8 papers · 1 filter
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…