8 papers · 1 filter
Manipulation via Force Distribution at Contact
Haegu Lee, Yitaek Kim, Casper Hewson Rask +1
Efficient and robust trajectories play a crucial role in contact-rich manipulation, which demands accurate mod- eling of object-robot interactions. Many existing approaches rely on…
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…