What we do

Research

From arm manipulation to mobile manipulators and humanoids — robots that work autonomously in dynamic environments.

Motion Planning

Planning feasible grasps and motions under kinematic, closed-chain, and task constraints — from single arms to whole-body systems.

Grasp Planning
Whole-body Motion Planning
Constrained Motion Planning

Related publications

Connectivity-Aware Representations for Constrained Motion Planning via Multi-Scale Contrastive Learning IEEE ICRA 2026arXiv
A Constrained Motion Planning Method Exploiting Learned Latent Space for High-Dimensional State and Constraint Spaces IEEE/ASME T-MECH 2024Paper
Motion Planning for Closed-Chain Constraints Based on Probabilistic Roadmap With Improved Connectivity IEEE/ASME T-MECH 2022Paper
Scalable Learned Geometric Feasibility for Cooperative Grasp and Motion Planning IEEE RA-L 2022Paper

Physical AI

Learning manipulation skills from data — imitation learning, vision-language-action models, teleoperation for data collection, and post-optimization of learned policies.

Post-optimization Algorithm
Vision-Language-Action Models
Imitation Learning
Teleoperation
Dataset Augmentation

Related publications

SNU-Avatar Haptic Arm: A Hybrid QDD/DD Operator Interface for Teleoperation IEEE Access 2026Paper
LiPo: A Lightweight Post-optimization Framework for Smoothing Action Chunks Generated by Learned Policies IJCAS 2025Paper
Intuitive and Interactive Robotic Avatar System for Tele-Existence: TEAM SNU in the ANA Avatar XPRIZE Finals IJSR 2024Paper