Hello everyone, I‘m Yan Yixian from Tohoku University,
and I’m reporting on work I did with Kazuki Takashima，
Anthony Tang, Takayuki Tanno, Kazuyuki Fujita and Yoshifumi Kitamura
while I was a graduate student at RIEC in Tohoku University.
The system concept is ZoomWalls
this is a set of dynamic walls that simulate the haptic infrastructure for room-scale VR worlds.
Adding physical feedback for virtual objects in VR
has been proven effective to help increase immersion.
Our interest is in exploring how we can do this for room-scale VR,
as this will help provide better experience of a virtual space.
Many use physical props that stand-in for VR objects—here,
the prop is a haptically representation of something in the environment.
TurkDeck uses human actuators dynamically to rearrange
a set of props in a room-scale VR experience.
This is used to create a number of really neat effects,
but the problem is that it relies heavily on human effectors to make work properly.
Another approach uses flexible robotic devices like robot arms.
Here, they only encounter the users’ touch when necessary.
These have not been effectively created for walking VR experiences in room-scale VR.
A recent effort called RoomShift，
introduces a new method using a set of autonomous robots
that carrying pieces of furniture around the room-scale virtual environment.
Similar to our approach,
these pieces of furniture can provide haptic feedback to users.
Our work builds on this proof of concept to explore
how autonomous robots can COORDINATE to provide a cohesive experience for room-scale VR.
We propose ZoomWalls, which relies on
a set of automatically coordinated wall-shape props.
Each ZoomWall is a human-size moving wall segment
which enables human-scale encounter-type haptic EX in room-scale VR scenario
By combining multiple ZoomWalls,
complex room structures,
and even maze-like structures can be physically represented.
In this case, you could see,
a corner is materialized in real world by 2 ZoomWalls.
The ZoomWalls system is compose of 2 software components.
A Predictor component predicts what walls we need to materialize for the user
—that is, which wall is the user going to be touching next.
It creates this prediction based on movement and position data.
A Dispatcher component coordinates ZoomWalls to determine
which physical ZoomWalls are needed to materialize specific walls.
The system tracks the location of each ZoomWalls through vive tracker,
and moves them using a Roomba robot.
We designed 2 prediction algorithms for the Predictor component.
The first one, we call it Bounding Box algorithm,
We consider a bounding box around the user is based on a straightforward collision detection approach.
We consider a bounding box around the user (so that it moves as the user moves and turns):
any virtual surface that enters this bounding box is flagged as a surface likely to be touched.
The problem with this approach is that Is that
the system creates some significant latency
when the number of ZoomWalls is not sufficient.
So, we designed the second algorithm,
where we combined bounding box with a motion machine learning algorithm.
We use a support vector machine to predict which surface
the user intends to touch. touch
Inputs for this SVM are motion data of the user during last second
how quickly they are moving, which direction they are facing, and so on.
We use this to determine which walls have the highest priority.
Then, the surfaces in the retained smaller bounding box only have normal priority.
To test our system, we conducted a study
where participants would play a VR escape room game with and without the ZoomWall system.
The task of participants is unlock a dark, four-walled room
by tracing five rune patterns on different walls.
To limit their walking speed,
they were told an invisible demon living in the room
could spot them if they move too quickly.
We test 3 conditions
and measured the actual latency of the prototype system
and user EX in using 7-point likert scale questions.
12 participants helped us in this study.
The result of this study is shown here.
The latency in both ZoomWall conditions are less than ignorable threshold.
In terms of subjective responses,
we focused on comparing the baseline and the 2 ZoomWall conditions。
In all three sub-scale, a same tendency can be observed: improve their experiments?
participants believe the ZoomWalls improved their VR EX.
Beyond the basic four-walled room,
we explored the ability of ZoomWalls
to represent different kinds of rooms like
narrow corridor and zig-zag room.
Based on these explorations,
we designed a proof-of-concept FPS game and an adventure game with ZoomWalls.
Unlike traditional VR theme parks,
ZoomWalls dynamically reconfigures the haptic infrastructure on demand
without the need of human intervention.
Beyond entertainment,
zoomwalls can also be applied into other VR experiences
that emphasize spatial awareness,
like tactical training, emergency drill and architectural design.
So In This research
we proposed the ZoomWalls that
Simulates the haptic infrastructure in room-scale VR
reling on autonomous robotic encounter-type wall-shaped props.
The future works include expanding prediction algorithm
for more general case,
supporting faster walking by stronger actuators
and representing more shapes by using shape-changing surfaces.
Okay. That's all.
Thanks for listening.