2 citations · 2 across the 11 of their papers we have counts for
6 papers · 2 filters
Tac2Motion: Contact-Aware Reinforcement Learning with Tactile Feedback for Robotic Hand Manipulation
Yitaek Kim, Casper Hewson Rask, Christoffer Sloth
This paper proposes Tac2Motion, a contact-aware reinforcement learning framework to facilitate the learning of contact-rich in-hand manipulation tasks, such as removing a lid. To t…
Solving Robotics Tasks with Prior Demonstration via Exploration-Efficient Deep Reinforcement Learning
Chengyandan Shen, Christoffer Sloth
This paper proposes an exploration-efficient Deep Reinforcement Learning with Reference policy (DRLR) framework for learning robotics tasks that incorporates demonstrations. The DR…
Robust Adaptive Time-Varying Control Barrier Function with Application to Robotic Surface Treatment
Yitaek Kim, Christoffer Sloth
Set invariance techniques such as control barrier functions (CBFs) can be used to enforce time-varying constraints such as keeping a safe distance from dynamic objects. However, ex…
Imitation Learning-Based Path Generation for the Complex Assembly of Deformable Objects
Yitaek Kim, Christoffer Sloth
This paper investigates how learning can be used to ease the design of high-quality paths for the assembly of deformable objects. Object dynamics plays an important role when manip…
Trajectory Optimization for In-Hand Manipulation with Tactile Force Control
Haegu Lee, Yitaek Kim, Victor Melbye Staven +1
The strength of the human hand lies in its ability to manipulate small objects precisely and robustly. In contrast, simple robotic grippers have low dexterity and fail to handle sm…
Safety-Ensured Robotic Control Framework for Cutting Task Automation in Endoscopic Submucosal Dissection
Yitaek Kim, Iñigo Iturrate, Christoffer Sloth +1
There is growing interest in automating surgical tasks using robotic systems, such as endoscopy for treating gastrointestinal (GI) cancer. However, previous studies have primarily…