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Analysis and synthesis of bipedal humanoid movement : a physical simulation approach
textAdvances in graphics and robotics have increased the importance of tools for synthesizing humanoid movements to control animated characters and physical robots. There is also an increasing need for analyzing human movements for clinical diagnosis and rehabilitation. Existing tools can be expensive, inefficient, or difficult to use. Using simulated physics and motion capture to develop an interactive virtual reality environment, we capture natural human movements in response to controlled stimuli. This research then applies insights into the mathematics underlying physics simulation to adapt the physics solver to support many important tasks involved in analyzing and synthesizing humanoid movement. These tasks include fitting an articulated physical model to motion capture data, modifying the model pose to achieve a desired configuration (inverse kinematics), inferring internal torques consistent with changing pose data (inverse dynamics), and transferring a movement from one model to another model (retargeting). The result is a powerful and intuitive process for analyzing and synthesizing movement in a single unified framework.Computer Science
Learning dynamic motor skills for terrestrial locomotion
The use of Deep Reinforcement Learning (DRL) has received significantly increased attention
from researchers within the robotics field following the success of AlphaGo, which demonstrated
the superhuman capabilities of deep reinforcement algorithms in terms of solving complex
tasks by beating professional GO players. Since then, an increasing number of researchers
have investigated the potential of using DRL to solve complex high-dimensional robotic tasks,
such as legged locomotion, arm manipulation, and grasping, which are difficult tasks to solve
using conventional optimization approaches.
Understanding and recreating various modes of terrestrial locomotion has been of long-standing interest to roboticists. A large variety of applications, such as rescue missions,
disaster responses and science expeditions, strongly demand mobility and versatility in legged
locomotion to enable task completion. In order to create useful physical robots, it is necessary
to design controllers to synthesize the complex locomotion behaviours observed in humans
and other animals.
In the past, legged locomotion was mainly achieved via analytical engineering approaches.
However, conventional analytical approaches have their limitations, as they require relatively
large amounts of human effort and knowledge. Machine learning approaches, such as DRL,
require less human effort compared to analytical approaches. The project conducted for this
thesis explores the feasibility of using DRL to acquire control policies comparable to, or better
than, those acquired through analytical approaches while requiring less human effort.
In this doctoral thesis, we developed a Multi-Expert Learning Architecture (MELA) that
uses DRL to learn multi-skill control policies capable of synthesizing a diverse set of dynamic
locomotion behaviours for legged robots. We first proposed a novel DRL framework for the
locomotion of humanoid robots. The proposed learning framework is capable of acquiring
robust and dynamic motor skills for humanoids, including balancing, walking, standing-up
fall recovery. We subsequently improved upon the learning framework and design a novel
multi-expert learning architecture that is capable of fusing multiple motor skills together in
a seamless fashion and ultimately deploy this framework on a real quadrupedal robot. The
successful deployment of learned control policies on a real quadrupedal robot demonstrates
the feasibility of using an Artificial Intelligence (AI) based approach for real robot motion control
Advances in Robotics, Automation and Control
The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man
๊ฐ์ํ์ค์์ ๋ชธ์ ์์ธ์ ๊ณต๊ฐ์ธ์ง, ๊ณต๊ฐ์ด๋๋ฐฉ๋ฒ, ์กด์ฌ๊ฐ, ์ฌ์ด๋ฒ๋ฉ๋ฏธ์ ์ํธ์์ฉ์ ๋ํ ์ฐ๊ตฌ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ์ธ๋ฌธ๋ํ ํ๋๊ณผ์ ์ธ์ง๊ณผํ์ ๊ณต, 2021. 2. ์ด๊ฒฝ๋ฏผ.๊ฐ์ํ์ค์ ๋ชธ๊ณผ ๋ง์์ด ๊ณต๊ฐ์ ํจ๊ป ์กด์ฌํ๋ค๋ ์ผ์์ ๊ฒฝํ์ ๋ํด ์๋ก์ด ๊ด์ ์ ์ ์ํ๋ค. ์ปดํจํฐ๋ก ๋งค๊ฐ๋ ์ปค๋ฎค๋์ผ์ด์
์์ ๋ง์ ๊ฒฝ์ฐ ์ฌ์ฉ์๋ค์ ๋ชธ์ ๋ฐฐ์ ๋๋ฉฐ ๋ง์์ ์กด์ฌ๊ฐ ์ค์ํ๋ค๊ณ ๋๋ผ๊ฒ ๋๋ค. ์ด์ ๊ด๋ จํ์ฌ ๊ฐ์ํ์ค์ ์ฌ์ฉ์๋ค์๊ฒ ์ปค๋ฎค๋์ผ์ด์
์ ์์ด ๋ฌผ๋ฆฌ์ ๋ชธ์ ์ญํ ๊ณผ ๋น์ฒดํ๋ ์ํธ์์ฉ์ ์ค์์ฑ์ ๋ํด ์ฐ๊ตฌํ ์ ์๋ ๊ธฐํ๋ฅผ ์ ๊ณตํ๋ค.
๊ธฐ์กด ์ฐ๊ตฌ์ ์ํ๋ฉด ์คํ, ์ฃผ์์ง์ค, ๊ธฐ์ต, ์ง๊ฐ๊ณผ ๊ฐ์ ์ธ์ง๊ธฐ๋ฅ๋ค์ด ๋ชธ์ ์์ธ์ ๋ฐ๋ผ ๋ค๋ฅด๊ฒ ์์ฉํ๋ค๊ณ ํ๋ค. ํ์ง๋ง ์ด์ ๊ฐ์ ์ธ์ง๊ธฐ๋ฅ๋ค๊ณผ ๋ชธ ์์ธ์ ์ํธ์ฐ๊ด์ฑ์ ์ฌ์ ํ ๋ช
ํํ ๋ฐํ์ง๊ณ ์์ง ์๋ค. ํนํ ๊ฐ์ํ์ค์์ ๋ชธ์ ์์ธ๊ฐ ์ง๊ฐ๋ฐ์์ ๋ํ ์ธ์ง๊ณผ์ ์ ์ด๋ค ์์ฉ์ ํ๋์ง์ ๋ํ ์ดํด๋ ๋งค์ฐ ๋ถ์กฑํ ์ํฉ์ด๋ค.
๊ฐ์ํ์ค ์ฐ๊ตฌ์๋ค์ ์กด์ฌ๊ฐ์ ๊ฐ์ํ์ค์ ํต์ฌ ๊ฐ๋
์ผ๋ก ์ ์ํ์์ผ๋ฉฐ ํจ์จ์ ์ธ ๊ฐ์ํ์ค ์์คํ
๊ตฌ์ฑ๊ณผ ๋ฐ์ ํ ๊ด๊ณ๊ฐ ์๋ค๊ณ ํ๋ค. ์กด์ฌ๊ฐ์ ๊ฐ์๊ณต๊ฐ์ ์๋ค๊ณ ๋๋ผ๋ ์์์ํ๋ฅผ ๋งํ๋ค. ๊ตฌ์ฒด์ ์ผ๋ก ๊ฐ์ํ์ค ์ ๊ฒฝํ์ ์ค์ฌ ์กด์ฌํ๋ค๊ณ ๋๋ผ๋ ์์์ํ๋ฅผ ๋งํ๋ค. ์ด๋ฐ ์กด์ฌ๊ฐ์ด ๋์ ์๋ก ํ์ค์ฒ๋ผ ์ธ์งํ๊ธฐ์ ์กด์ฌ๊ฐ์ ๊ฐ์ํ์ค ๊ฒฝํ์ ์ธก์ ํ๋ ์ค์ํ ์งํ์ด๋ค. ๋ฐ๋ผ์ ๊ฐ์๊ณต๊ฐ์ ์กด์ฌํ๊ณ ์๋ค๋ ์์์ ๊ฒฝํ ((๊ฑฐ๊ธฐ์ ์๋ค(being there)), ์ฆ ์กด์ฌ๊ฐ์ ๋งค๊ฐ๋ ๊ฐ์๊ฒฝํ๋ค์ ์ธ์ง ์ฐ๊ตฌ์ ์ค์ํ ๊ฐ๋
์ด๋ค.
๊ฐ์ํ์ค์ ์ฌ์ด๋ฒ๋ฉ๋ฏธ๋ฅผ ์ ๋ฐํ๋ ๊ฒ์ผ๋ก ์๋ ค์ ธ ์๋ค. ์ด ์ฆ์์ ๊ฐ์ํ์ค์ ์ฌ์ฉ์ฑ์ ์ ์ฝํ๋ ์ฃผ์ ์์ธ์ผ๋ก ํจ๊ณผ์ ์ธ ๊ฐ์ํ์ค ๊ฒฝํ์ ์ํด ์ฌ์ด๋ฒ๋ฉ๋ฏธ์ ๋ํ ๋ค์ํ ์ฐ๊ตฌ๊ฐ ํ์ํ๋ค. ์ฌ์ด๋ฒ๋ฉ๋ฏธ๋ ๊ฐ์ํ์ค ์์คํ
์ ์ฌ์ฉํ ๋ ๋ํ๋๋ฉฐ ์ด์ง๋ฌ์, ๋ฐฉํฅ์์ค, ๋ํต, ๋ํ๋ฆผ, ๋ํผ๋ก๋๋ฑ์ ์ฆ์์ ํฌํจํ๋ค. ์ด๋ฐ ์ฌ์ด๋ฒ๋ฉ๋ฏธ์๋ ๊ฐ์ธ์ฐจ, ์ฌ์ฉ๋ ๊ธฐ์ , ๊ณต๊ฐ๋์์ธ, ์ํ๋ ์
๋ฌด๋ฑ ๋งค์ฐ ๋ค์ ์์ธ๋ค์ด ๊ด์ฌํ๊ณ ์์ด ๋ช
ํํ ์์ธ์ ๊ท์ ํ ์ ์๋ค. ์ด๋ฐ ๋ฐฐ๊ฒฝ์ผ๋ก ์ธํด ์ฌ์ด๋ฒ๋ฉ๋ฏธ ์ ๊ฐ๊ณผ ๊ด๋ จํ ๋ค์ํ ์ฐ๊ตฌ๋ค์ด ํ์ํ๋ฉฐ ์ด๋ ๊ฐ์ํ์ค ๋ฐ์ ์ ์ค์ํ ์๋ฏธ๋ฅผ ๊ฐ๋๋ค.
๊ณต๊ฐ์ธ์ง๋ 3์ฐจ์ ๊ณต๊ฐ์์ ์ ์ฒด ์์ง์๊ณผ ๋์๊ณผ์ ์ํธ์์ฉ์ ์ค์ํ ์ญํ ์ ํ๋ ์ธ์ง์์คํ
์ด๋ค. ๊ฐ์๊ณต๊ฐ์์ ์ ์ฒด ์์ง์์ ๋ค๋น๊ฒ์ด์
, ์ฌ๋ฌผ์กฐ์, ๋ค๋ฅธ ์์ด์ ํธ๋ค๊ณผ ์ํธ์์ฉ์ ๊ด์ฌํ๋ค. ํนํ ๊ฐ์๊ณต๊ฐ์์ ๋ค๋น๊ฒ์ด์
์ ์์ฃผ ์ฌ์ฉ๋๋ ์ค์ํ ์ํธ์์ฉ ๋ฐฉ์์ด๋ค. ์ด์ ๊ฐ์๊ณต๊ฐ์ ๋ค๋น๊ฒ์ด์
ํ ๋ ์กด์ฌ๊ฐ์ ์ํฅ์ ์ฃผ์ง ์๊ณ ๋ฉ๋ฏธ์ฆ์์ ์ ๋ฐํ์ง ์๋ ํจ๊ณผ์ ์ธ ๊ณต๊ฐ์ด๋ ๋ฐฉ๋ฒ์ ๋ํ ๋ค์ํ ์ฐ๊ตฌ๋ค์ด ์ด๋ฃจ์ด์ง๊ณ ์๋ค.
์ด์ ์ฐ๊ตฌ๋ค์ ์ํ๋ฉด ์์ ์ด ์กด์ฌ๊ฐ๊ณผ ์ฒดํ๊ฐ์ ์ํฅ์ ์ค๋ค๊ณ ํ๋ค. ์ด๋ ์์ ์ ๋ฐ๋ผ ์ฌ์ฉ์์ ํ๋๊ณผ ๋์๋ค๊ณผ์ ์ํธ์์ฉ ๋ฐฉ์์ ๋ฌ๋ผ์ง๊ธฐ ๋๋ฌธ์ด๋ค. ๋ฐ๋ผ์ ๊ฐ์๊ณต๊ฐ์์ ๊ฒฝํ ๋ํ ์์ ์ ๋ฐ๋ผ ๋ฌ๋ผ์ง๋ค. ์ด๋ฐ ๋ฐฐ๊ฒฝ์ผ๋ก ๋ชธ์ ์์ธ, ๊ณต๊ฐ์ธ์ง, ์ด๋๋ฐฉ๋ฒ, ์กด์ฌ๊ฐ, ์ฌ์ด๋ฒ๋ฉ๋ฏธ์ ์ํธ ์ฐ๊ด์ฑ์ ๋ํ ์ฐ๊ตฌ๋ฅผ ์์ ์ ๋ฐ๋ผ ๋ถ๋ฅํด์ ์ฐ๊ตฌํ ํ์๊ฐ ์๋ค. ์ด๋ฅผ ํตํด ๊ฐ์ํ์ค ์ ๊ณต๊ฐ ๋ค๋น๊ฒ์ด์
์ ๋ํ ์ธ์ง๊ณผ์ ์ ๋ณด๋ค ๋ค๊ฐ์ ์ผ๋ก ์ดํด ํ ์ ์์ ๊ฒ์ด๋ค.
๊ทธ๋์ ์กด์ฌ๊ฐ๊ณผ ์ฌ์ด๋ฒ ๋ฉ๋ฏธ์ ๋ด์ฌ๋ ๋งค์ปค๋์ฆ์ ์ดํดํ๊ธฐ ์ํด ๋ค์ํ ์ฐ๊ตฌ๋ค์ด ์งํ๋์ด ์๋ค. ํ์ง๋ง ๋ชธ์ ์์ธ์ ๋ฐ๋ฅธ ์ธ์ง์์ฉ์ด ์กด์ฌ๊ฐ๊ณผ ์ฌ์ด๋ฒ๋ฉ๋ฏธ์ ์ด๋ค ์ํฅ์ ์ฃผ๋์ง์ ๋ํ ์ฐ๊ตฌ๋ ๊ฑฐ์ ์ด๋ฃจ์ด์ง์ง ์์๋ค. ์ด์ ๋ณธ ํ์๋
ผ๋ฌธ์์๋ 1์ธ์นญ๊ณผ 3์ธ์นญ ์์ ์ผ๋ก ๋ถ๋ฅ๋ ๋ณ๋์ ์คํ๊ณผ ์ฐ๊ตฌ๋ฅผ ์งํํ์ฌ ๊ฐ์ํ์ค์์ ๋ชธ์ ์์ธ์ ๊ณต๊ฐ์ธ์ง, ๊ณต๊ฐ์ด๋๋ฐฉ๋ฒ, ์กด์ฌ๊ฐ, ์ฌ์ด๋ฒ๋ฉ๋ฏธ์ ์ํธ์ฐ๊ด์ฑ์ ๋ณด๋ค ์ฌ์ธต์ ์ผ๋ก ์ดํดํ๊ณ ์ ํ๋ค.
์ 3์ฅ์์๋ 3์ธ์นญ์์ ์ ์คํ๊ณผ ๊ฒฐ๊ณผ์ ๋ํ ๋ด์ฉ์ ๊ธฐ์ ํ๋ค. 3์ธ์นญ์์ ์คํ์์๋ ๊ฐ์๊ณต๊ฐ์์ ๋ชธ์ ์์ธ์ ์กด์ฌ๊ฐ์ ์ํธ์ฐ๊ด์ฑ ์ฐ๊ตฌ๋ฅผ ์ํด ์ธ๊ฐ์ง ๋ชธ์ ์์ธ (์์๋ ์์ธ, ์์ ์์ธ, ๋ค๋ฆฌ๋ฅผ ํด๊ณ ์์ ์์ธ)์ 2๊ฐ์ง ํ์
์ ๊ณต๊ฐ์ด๋ ์์ ๋ (๋ฌดํ, ์ ํ)๋ฅผ ์ํธ ๋น๊ตํ๋ค. ์คํ๊ฒฐ๊ณผ์ ์ํ๋ฉด ๊ณต๊ฐ์ด๋ ์์ ๋๊ฐ ๋ฌดํํ ๊ฒฝ์ฐ ์์๋ ์์ธ์์ ์กด์ฌ๊ฐ์ด ๋๊ฒ ๋ํ๋ฌ๋ค. ์ถ๊ฐ์ ์ผ๋ก ๊ฐ์๊ณต๊ฐ์์ ๋ชธ์ ์์ธ์ ์กด์ฌ๊ฐ์ ๊ณต๊ฐ์ด๋์์ ๋์ ๊ด๋ จ์ด ์๋ ๊ฒ์ผ๋ก ๋ํ๋ฌ์ผ๋ฉฐ ์ฌ๋ฌ ์ธ์ง๊ธฐ๋ฅ ์ค ์ฃผ์์ง์ค์ด ๋ชธ์ ์์ธ, ์กด์ฌ๊ฐ, ๊ณต๊ฐ์ธ์ง์ ํตํฉ์ ์ํธ์์ฉ์ ์ด๋์ด ๋ธ ๊ฒ์ผ๋ก ํ์
๋์๋ค. 3์ธ์นญ์์ ์ ๊ฒฐ๊ณผ๋ค์ ์ข
ํฉํด ๋ณด๋ฉด ๋ชธ ์์ธ์ ์ธ์ง์ ์ํฅ์ ๊ณต๊ฐ์ด๋์์ ๋์ ์๊ด๊ด๊ณ๊ฐ ์๋ ๊ฒ์ผ๋ก ์ถ์ธกํ ์ ์๋ค.
์ 4์ฅ์์๋ 1์ธ์นญ์์ ์ ์คํ๊ณผ ๊ฒฐ๊ณผ์ ๋ํ ๋ด์ฉ์ ๊ธฐ์ ํ๋ค. 1์ธ์นญ์์ ์คํ์์๋ ๊ฐ์๊ณต๊ฐ์์ ๋ชธ์ ์์ธ, ๊ณต๊ฐ์ด๋๋ฐฉ๋ฒ, ์กด์ฌ๊ฐ, ์ฌ์ด๋ฒ๋ฉ๋ฏธ์ ์ํธ์ฐ๊ด์ฑ ์ฐ๊ตฌ๋ฅผ ์ํด ๋ ์กฐ๊ฑด์ ๋ชธ์ ์์ธ (์์๋ ์์ธ, ์์ ์๋ ์์ธ)์ ๋ค๊ฐ์ง ํ์
์ ์ด๋๋ฐฉ๋ฒ (์คํฐ์ด๋ง + ๋ชธ์ ํ์ฉํ ํ์ , ์คํฐ์ด๋ง + ๋๊ตฌ๋ฅผ ํ์ฉํ ํ์ , ํ
๋ ํฌํ
์ด์
+ ๋ชธ์ ์ด์ฉํ ํ์ , ํ
๋ ํฌํ
์ด์
+ ๋๊ตฌ๋ฅผ ํ์ฉํ ํ์ )์ ์ํธ ๋น๊ต๊ฐ ์ด๋ฃจ์ด ์ก๋ค. ์คํ๊ฒฐ๊ณผ์ ์ํ๋ฉด ์์น์ด๋๋ฐฉ์๊ณผ ํ์ ๋ฐฉ์์ ๋ฐ๋ฅธ ๊ณต๊ฐ์ด๋์์ ๋๋ ์ฑ๊ณต์ ์ธ ๋ค๋น๊ฒ์ด์
๊ณผ ๊ด๋ จ์ด ์์ผ๋ฉฐ ์กด์ฌ๊ฐ์ ์ํฅ์ ์ฃผ๋ ๊ฒ์ผ๋ก ๋ํ๋ฌ๋ค. ์ถ๊ฐ์ ์ผ๋ก ์ฐ์์ ์ผ๋ก ์๊ฐ์ ๋ณด๊ฐ ์
๋ ฅ๋๋ ์คํฐ์ด๋ง ๋ฐฉ๋ฒ์ ์๊ฐ์ด๋์ ๋์ฌ ๋น์ฐ์์ ๋ฐฉ๋ฒ์ธ ํ
๋ ํฌํ
์ด์
๋ณด๋ค ์ฌ์ด๋ฒ๋ฉ๋ฏธ๋ฅผ ๋ ์ ๋ฐํ๋ ๊ฒ์ผ๋ก ๋ํ๋ฌ๋ค. 1์ธ์นญ์์ ์ ๊ฒฐ๊ณผ๋ค์ ์ข
ํฉํด ๋ณด๋ฉด ๊ฐ์๊ณต๊ฐ์์ ๋ค๋น๊ฒ์ด์
์ ํ ๋ ์กด์ฌ๊ฐ๊ณผ ์ฌ์ด๋ฒ๋ฉ๋ฏธ๋ ๊ณต๊ฐ์ด๋๋ฐฉ๋ฒ๊ณผ ๊ด๋ จ์ด ์๋ ๊ฒ์ผ๋ก ๊ฐ์ ํ ์ ์๋ค.
์ 3์ฅ์ 3์ธ์นญ ์์ ์คํ๊ฒฐ๊ณผ์ ์ํ๋ฉด ๋ชธ์ ์์ธ์ ์กด์ฌ๊ฐ์ ์๊ด๊ด๊ณ๊ฐ ์๋ ๊ฒ์ผ๋ก ์ ์๋์๋ค. ๋ฐ๋ฉด ์ 4์ฅ์ ์คํ๊ฒฐ๊ณผ์ ์ํ๋ฉด 1์ธ์นญ์์ ์ผ๋ก ๊ฐ์๊ณต๊ฐ์ ๋ค๋น๊ฒ์ด์
ํ ๋๋ ๊ณต๊ฐ์ด๋๋ฐฉ๋ฒ์ด ์กด์ฌ๊ฐ๊ณผ ์ฌ์ด๋ฒ๋ฉ๋ฏธ์ ์ํฅ์ ์ฃผ๋ ๊ฒ์ผ๋ก ๋ํ๋ฌ๋ค. ์ด ๋ ์คํ์ ๋ํ ์ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ํตํด ๊ฐ์ํ์ค์์ ๋ชธ์ ์์ธ์ ๊ณต๊ฐ์ธ์ง (๋ค๋น๊ฒ์ด์
)์ ์ํธ์ฐ๊ด์ฑ์ ๋ํ ์ดํด๋ฅผ ํ๋ํ๊ณ ์กด์ฌ๊ฐ ๋ฐ ์ฌ์ด๋ฒ๋ฉ๋ฏธ์ ๊ณต๊ฐ์ด๋๋ฐฉ๋ฒ์ ๊ด๋ จ์ฑ์ ๋ฐํ ์ ์์ ๊ฒ์ผ๋ก ๊ธฐ๋ํ๋ค.Immersive virtual environments (VEs) can disrupt the everyday connection between where our senses tell us we are and where we are actually located. In computer-mediated communication, the user often comes to feel that their body has become irrelevant and that it is only the presence of their mind that matters. However, virtual worlds offer users an opportunity to become aware of and explore both the role of the physical body in communication, and the implications of disembodied interactions.
Previous research has suggested that cognitive functions such as execution, attention, memory, and perception differ when body position changes. However, the influence of body position on these cognitive functions is still not fully understood. In particular, little is known about how physical self-positioning may affect the cognitive process of perceptual responses in a VE.
Some researchers have identified presence as a guide to what constitutes an effective virtual reality (VR) system and as the defining feature of VR. Presence is a state of consciousness related to the sense of being within a VE; in particular, it is a โpsychological state in which the virtuality of the experience is unnoticedโ. Higher levels of presence are considered to be an indicator of a more successful media experience, thus the psychological experience of โbeing thereโ is an important construct to consider when investigating the association between mediated experiences on cognition.
VR is known to induce cybersickness, which limits its application and highlights the need for scientific strategies to optimize virtual experiences. Cybersickness refers to the sickness associated with the use of VR systems, which has a range of symptoms including nausea, disorientation, headaches, sweating and eye strain. This is a complicated problem because the experience of cybersickness varies greatly between individuals, the technology being used, the design of the environment, and the task being performed. Thus, avoiding cybersickness represents a major challenge for VR development.
Spatial cognition is an invariable precursor to action because it allows the formation of the necessary mental representations that code the positions of and relationships among objects. Thus, a number of bodily actions are represented mentally within a depicted VR space, including those functionally related to navigation, the manipulation of objects, and/or interaction with other agents. Of these actions, navigation is one of the most important and frequently used interaction tasks in VR environments. Therefore, identifying an efficient locomotion technique that does not alter presence nor cause motion sickness has become the focus of numerous studies.
Though the details of the results have varied, past research has revealed that viewpoint can affect the sense of presence and the sense of embodiment. VR experience differs depending on the viewpoint of a user because this vantage point affects the actions of the user and their engagement with objects. Therefore, it is necessary to investigate the association between body position, spatial cognition, locomotion method, presence, and cybersickness based on viewpoint, which may clarify the understanding of cognitive processes in VE navigation.
To date, numerous detailed studies have been conducted to explore the mechanisms underlying presence and cybersickness in VR. However, few have investigated the cognitive effects of body position on presence and cybersickness. With this in mind, two separate experiments were conducted in the present study on viewpoint within VR (i.e., third-person and first-person perspectives) to further the understanding of the effects of body position in relation to spatial cognition, locomotion method, presence, and cybersickness in VEs.
In Chapter 3 (Experiment 1: third-person perspective), three body positions (standing, sitting, and half-sitting) were compared in two types of VR game with a different degree of freedom in navigation (DFN; finite and infinite) to explore the association between body position and the sense of presence in VEs. The results of the analysis revealed that standing has the most significant effect on presence for the three body positions that were investigated. In addition, the outcomes of this study indicated that the cognitive effect of body position on presence is associated with the DFN in a VE. Specifically, cognitive activity related to attention orchestrates the cognitive processes associated with body position, presence, and spatial cognition, consequently leading to an integrated sense of presence in VR. It can thus be speculated that the cognitive effects of body position on presence are correlated with the DFN in a VE.
In Chapter 4 (Experiment 2: first-person perspective), two body positions (standing and sitting) and four types of locomotion method (steering + embodied control [EC], steering + instrumental control [IC], teleportation + EC, and teleportation + IC) were compared to examine the relationship between body position, locomotion method, presence, and cybersickness when navigating a VE. The results of Experiment 2 suggested that the DFN for translation and rotation is related to successful navigation and affects the sense of presence when navigating a VE. In addition, steering locomotion (continuous motion) increases self-motion when navigating a VE, which results in stronger cybersickness than teleportation (non-continuous motion). Overall, it can be postulated that presence and cybersickness are associated with the method of locomotion when navigating a VE.
In this dissertation, the overall results of Experiment 1 suggest that the cognitive influence of presence is body-dependent in the sense that mental and brain processes rely on or are affected by the physical body. On the other hand, the outcomes of Experiment 2 illustrate the significant effects of locomotion method on the sense of presence and cybersickness during VE navigation. Taken together, the results of this study provide new insights into the cognitive effects of body position on spatial cognition (i.e., navigation) in VR and highlight the important implications of locomotion method on presence and cybersickness in VE navigation.Chapter 1. Introduction 1
1.1. An Introductory Overview of the Conducted Research 1
1.1.1. Presence and Body Position 1
1.1.2. Navigation, Cybersickness, and Locomotion Method 3
1.2. Research Objectives 6
1.3. Research Experimental Approach 7
Chapter 2. Theoretical Background 9
2.1. Presence 9
2.1.1. Presence and Virtual Reality 9
2.1.2. Presence and Spatiality 10
2.1.3. Presence and Action 12
2.1.4. Presence and Attention 14
2.2. Body Position 16
2.2.1. Body Position and Cognitive Effects 16
2.2.2. Body Position and Postural Control 18
2.2.3. Body Position and Postural Stability 19
2.3. Spatial Cognition: Degree of Freedom in Navigation 20
2.3.1. Degree of Freedom in Navigation and Decision-Making 20
2.4. Cybersickness 22
2.4.1. Cybersickness and Virtual Reality 22
2.4.2. Sensory Conflict Theory 22
2.4.3. Postural Instability Theory 23
2.5. Self-Motion 25
2.5.1. Vection and Virtual Reality 25
2.5.2. Self-Motion and Navigation in a VE 27
2.6. Navigation in Virtual Environments 29
2.6.1. Translation and Rotation in Navigation 29
2.6.2. Spatial Orientation and Embodiment 32
2.6.3. Locomotion Methods 37
2.6.4. Steering and Teleportation 38
Chapter 3. Experiment 1: Third-Person Perspective 40
3.1. Quantification of the Degree of Freedom in Navigation 40
3.2. Experiment
3.2.1. Experimental Design and Participants 41
3.2.2. Stimulus Materials 42
3.2.2.1. First- and Third-person Perspectives in Gameplay 43
3.2.3. Experimental Setup and Process 44
3.2.4. Measurements 45
3.3. Results 45
3.3.1. Presence: two-way ANOVA 45
3.3.2. Presence: one-way ANOVA 46
3.3.2.1. Finite Navigation Freedom 46
3.3.2.2. Infinite Navigation Freedom 47
3.3.3. Summary of the Results 48
3.4. Discussion 49
3.4.1. Presence and Body Position 49
3.4.2. Degree of Freedom in Navigation and Decision-Making 50
3.4.3. Gender Difference and Gameplay 51
3.5. Limitations 52
Chapter 4. Experiment 2: First-Person Perspective 53
4.1. Experiment 53
4.1.1. Experimental Design and Participants 53
4.1.2. Stimulus Materials 54
4.1.3. Experimental Setup and Process 55
4.1.4. Measurements 56
4.2. Results 57
4.2.1. Presence: two-way ANOVA 58
4.2.2. Cybersickness: two-way ANOVA 58
4.2.3. Presence: one-way ANOVA 60
4.2.3.1. Standing Position 60
4.2.3.2. Sitting Position 60
4.2.4. Cybersickness: one-way ANOVA 62
4.2.4.1. Standing Position 62
4.2.4.2. Sitting Position 62
4.2.5. Summary of the Results 63
4.3. Discussion 65
4.3.1. Presence
4.3.1.1. Presence and Locomotion Method 66
4.3.1.2. Presence and Body Position 68
4.3.2. Cybersickness
4.3.2.1. Cybersickness and Locomotion Method 69
4.3.2.2. Cybersickness and Body Position 70
4.4. Limitations 71
Chapter 5. Conclusion 72
5.1. Summary of Findings 72
5.2. Future Research Direction 73
References 75
Appendix A 107
Appendix B 110
๊ตญ๋ฌธ์ด๋ก 111Docto
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