Based on machine learning and bioelectrical signal,
we are studying how to understand and represent human motion and
how to deliver and train motion skills to robots.
Background and Objectives
- Human motor skills differentiated from conventional robots in that they actively utilize the flexibility of the musculoskeletal system
- Existing flexible robots standing out only in terms of interacting with objects or environments
- Developing intelligent robots that reproduce or transcend
- dynamic, elastic, and explosive human motor skills
- motor skills expressing emotions in a sophisticated and delicate tasks
Motivation
- By merging motion information and muscle activities,
- Estimate force or power generated by the muscle
- Estimate mechanical impedance of joint or body segment
- Predict motion intention before motion
Topics
- To measure human motion
- Estimate the use of muscle elasticity for explosive tasks
- Estimate the interacting mechanical impedance in delicate tasks
- To discriminate and evaluate human motion
- Organize and express motion path in an implicit and hierarchical way
- Express and generate motion with mechanical impedance
- Analyze motion data to understand human motion principles
- Find out what differences occur physically according to the skill level
- To reproduce or transcend human motion by a robot
- Develop a robot system that implements dynamic/explosive movement of humans or delicate and flexible movements
- Develop impedance robot control algorithm overcoming motion differences
Last Updated on September 15, 2020