Myo-Taeg Lim

A Whole-Body Control Framework for Human-Like Walking with Knee Stretch on Flat-Foot Humanoids

Modular Actuator for Multimodal Proprioceptive and Kinesthetic Feedback of Robotic Hands

This study addresses the challenge of implementing proprioceptive and kinesthetic (PK) feedback in robotic hands, essential for grasping and manipulation tasks in unstructured environments. We developed a compact modular actuator featuring a low-module, high-transmission-ratio multistage gear mechan…

Single-Instance Sampling for Computationally Efficient and Accurate Real-Time Task Space MPPI Control

This study presents a model predictive path integral (MPPI) method capable of conducting high-frequency real-time model predictive control (MPC) for robot manipulators. Real-time MPC-based manipulation holds significant potential for controlling an end-effector precisely and reactively while satisfy…

Modular Actuator for Multimodal Proprioceptive and Kinesthetic Feedback of Robotic Hands

This study addresses the challenge of implementing proprioceptive and kinesthetic (PK) feedback in robotic hands, essential for grasping and manipulation tasks in unstructured environments. We developed a compact modular actuator featuring a low-module, high-transmission-ratio multistage gear mechan

Safe and Efficient Target Singulation with Multi-Fingered Gripper using Collision-Free Push-Stack Synergy

Target singulation involves a rearrangement of surrounding obstacles to create space for grasping the target. However, when objects are tightly packed in a confined workspace (i.e., a limited table boundary), it is not easy to relocate obstacles. Using non-prehensile manipulation, such as push, is s

Efficient Target Singulation with Multi-fingered Gripper using Propositional Logic

When multiple tablewares are closely packed on a table, rearranging obstacles to make space is necessary to grasp the target, often called target singulation. Due to the nature of handling fragile tablewares (i.e. plates, bowls), we make a few assumptions for the target singulation problem. First, t…

Style Blind Domain Generalized Semantic Segmentation via Covariance Alignment and Semantic Consistence Contrastive Learning

Deep learning models for semantic segmentation often experience performance degradation when deployed to unseen target domains unidentified during the training phase. This is mainly due to variations in image texture (i.e. style) from different data sources. To tackle this challenge existing domain…