19 citations · 23 across the 23 of their papers we have counts for
14 papers · 1 filter
Efficient Real-World Online Reinforcement Learning for Robot Manipulation via Centralized Training and Critic Decomposition
Changhao Li, Yifang Zhang, Heng Zhang +6
Real-world online reinforcement learning (RL) provides a promising approach for training robotic manipulation policies directly in the physical world, avoiding the sim-to-real gap…
Robots Need More than VLA and World Models
Elis Karcini, Faisal Mehrban, Quang Nguyen +6
Generalist robot intelligence is often framed as a policy-scaling problem: collect more robot demonstrations, train larger Vision-Language-Action (VLA) models, and expect broader g…
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 +3
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…