9 papers · 1 filter
Safety-Critical Control for Smoothed Implicit Contact Dynamics
Haegu Lee, Yitaek Kim, Christoffer Sloth
Smoothed implicit contact dynamics enables gradient-based planning and control for contact-rich tasks without predefined mode sequences. However, safety-critical control remains ch…
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…
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…
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…
Robust Adaptive Safe Robotic Grasping with Tactile Sensing
Yitaek Kim, Jeeseop Kim, Albert H. Li +2
Robotic grasping requires safe force interaction to prevent a grasped object from being damaged or slipping out of the hand. In this vein, this paper proposes an integrated framewo…
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…