19 citations · 22 across the 6 of their papers we have counts for
6 papers · 1 filter
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…
ActivePose: Active 6D Object Pose Estimation and Tracking for Robotic Manipulation
Sheng Liu, Zhe Li, Weiheng Wang +6
Accurate 6-DoF object pose estimation and tracking are critical for reliable robotic manipulation. However, zero-shot methods often fail under viewpoint-induced ambiguities and fix…
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…
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…