snu robotics lab서울... · 2020. 9. 29. · snu robotics lab lab introduction 2020. members ......

SNU Robotics LabLab Introduction 2020

Members

▪ BS EECS, MIT, 1985▪ PhD Applied Math., Harvard U, 1991▪ Asst. Prof., UC Irvine, 1991-1994▪ Professor, SNU, 1995-present▪ Adjunct Prof., Interactive Computing

Dept., Georgia Tech, 2009-2013▪ HKUST Robotics Institute, 2016-present▪ IEEE Fellow, IEEE RAS Distinguished

Lecturer, SNU Teaching Award 2014▪ Editor-in-Chief, IEEE Trans. Robotics▪ Edx Course Developer (Robot

Mechanics and Control I, II)▪ Co-Author of “Modern Robotics:

Mechanics, Planning, and Control”. 2017

▪ President of IEEE RAS., 2022-2023

Prof. Frank C. Park

13 PhD Candidates, 8 MS Candidates

Research Areas

Robotics

Machine Learning

ComputerVision

Robotics

• Physically consistent multibody inertial parameter identification using geometric algorithm

Robot Modeling

• RRT algorithm that uses an integral function of control effort in a vector field defined in the configuration space

• Adaptive stepsize RRT algorithm which solved cumbersome stepsize-tuning issue

Motion Planning

• A minimum attention control law for ball catching

• Energy based performance criteria for the dynamics-based optimization of robot trajectories

Robot Control

Randomized path planning on vector fields

A Geometric Algorithm for Robust Multibody Inertial Parameter Identification

Dynamics-Based Robot Motion Optimization

Machine Learning

• Coordinate invariant mapping functionals and distortion measures for assessment of closeness of mapping to isometry

• Metric learning for enhanced reliability and less biased estimation

Mathematical foundations of machine learning

• Prediction model for exoskeletons- Enables natural movement that better supports human gait

• Collision detection based on data retrieved from momentum observer

Machine learning in robotics

Industrial applications

Time-series anomaly detection

Original loss

Adversarial loss

Original dataset

Outlier

Negative dataset

Synthetic Outlier

Region represented by dictionary

Movement Prediction

• Time series anomaly detection using adversarial dictionary learning- Learns optimal dictionary that sufficiently expresses time series data- Adversarial training constrains dictionary from including expressions for anomalies

Computer Vision

Object detection• Novel formulations for object detection problem based on

measure theory and information geometry

Vision inspection• Neural network compression via transfer learning for automated

machine vision inspection Object detection on aerial images

Machine vision inspection

Projects

Kinodynamic Model Identification: A Unified Geometric Approach

Introduced a unified method that identify kinematics and dynamics parameter to reduce calibration error

Deep Reinforcement Learning Algorithm Development for Industrial Robot

Unstable motion

Aggressive exploration

Performance

Admittance environment

Motion planning

Velocity control

Acceleration control

Developed safe, efficient reinforcement learning algorithm for position control of industrial robot

Locomotion Characteristics Analysis and Motion Control of Terrestrial Organisms

Mimic the movement of terrestrial organisms and optimize feed back control

Water strider robot simulation Jumping-gliding robot simulationRHex robot simulation

Website

http://robotics.snu.ac.kr/fcp/