We propose a method to generate android’s body gesture by real-time reconfiguring a large-scaled motion database which captured humans’ motions. Realization of human-robot interaction (HRI) is crucial for the future direction of human-robot symbiosis. Especially, natural body gesture for visual information, as well as speech dialog for audio information, is very important for human-like robots (e.g., humanoids and androids). First, we propose to morph motions with some semantic meaning based on Bags-of-words based motion retrieval and Match-Web. Next, we propose to interpolate several types of motions based on Motion Graph and sampling-based path planning. We propose to connect necessary components by Key-Value Store. We actually evaluated the effectiveness of the proposed methods by helps of over 1,700 subjects who attend the event. As a future direction, we also describe how to enlarge motion database and how to obtain human natural activities.

1. Human-Robot Interaction by
an Android Robot Based on
Natural Motion Database
Jun Takamatsu, Robotics Lab., NAIST
http://robotics.naist.jp/

2. Goal
• Naturally interact with human in real-time
2

3. Issues in Human-Robot Interaction (HRI)
• Naturalness of robot’s motion
• Change of environment
• Interruption for smooth interaction
3

4. Our Proposal [Kondo ’13]
• Use motion database captured from human
4
Motion Graph [Kovar ’02]
RRT [Kuffner ’00]
Interpolation of primitives
while considering collision avoidance
Match Web
[Kovar ’04]
Morphing of primitives
while keeping its naturalness
Adapt sudden interruption
Adjust target position following
environmental changes
Target position
Generate various motion in real-time,
while keeping naturalness of motion database

5. Preparation of Motion
Database
1. Classify motions with respect to their types
2. Parameterize each type of motion
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（φ, θ）
BOWING
（φ, θ, b）
SPREADING
（x, y, z）
POINTING

6. Morph by Match Web
• Generate motion by weighted sum of motions in
the same class
6
Distance between two motions
SD (parameter)

7. Interpolate Motion Primitives
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Primitive A
Primitive C
Primitive B
Plan path D using probabilistic
roadmap method
Interruption
Smoothly interpolate primitive A & B
using polynomial functions
Interpolate
• Primitive A & planned path D
• Primitive C & planned path D

8. HRI-Specified Motion Planning
8
1. Generate candidate 2. Expand roadmaps
3. Connect roadmaps 4. Generate trajectory
Limit the search region
around the trajectory
Modify parts of trajectory that
collide the environment by
multi-thread RRT
Interpolate nearer roadmaps Smooth connected roadmap

9. IMPLEMENTATION
9

10. System Overview
10

11. Key-Value Store
• Every system communicates each other through
Key-Value Store
(e.g. Mediator in design pattern)
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Key Value
Talker’s
position
(0, 1, 1.7)
# of persons 5
Keyword
{ “you”=100, “who”=70,
“Who are you?”=20 }
Type of gesture
[ POINTING_MYSELF,
POINTING ]
: :
GET
POST, PUT,
DELETE
HTTP
Key-Value Store
Output
Component
Output
Component
Output
component
Planning
Component
Intermediate
component
Input
Component
Input
Component
Input
Component
Input
component

12. EXPERIMENTS
12

13. Experimental Scene
• Event “Celebrating the 1300th Anniversary of
Nara Heijo-kyo Capital in 2010”
→ Analyze 1,662 voluntary participants
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Microphone
Camera
Actroid-SIT

14. Examples of Participants
14

15. Review of Our Proposal
15
Parametric vs Non-parametric Interruptible vs Un-interruptible
Motion Graph [Kovar ’02]
RRT [Kuffner ’00]
Interpolation of primitives
while considering collision avoidance
Match Web
[Kovar ’04]
Morphing of primitives
while keeping its naturalness
Adapt sudden interruption
Adjust target position following
environmental changes
Target position

16. Experimental Condition
• Conduct each condition each day
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non-parametric parametric
un-interruptible
First day
467
Second day
429
interruptible
Third day
432
Fourth day
334
Morphing of primitives
Adapt sudden
interruption

17. Quantitative Evaluation
• Ratio of participants to audiences
→Aggressiveness of participation
• Residence time
→Attractiveness of HRI system
17

18. Ratio of Speakers
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un-interruptible
interruptible
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
non-parametric
parametric
0.48
0.61
0.38
0.62
**：p < 0.01

19. Residence Time
19
un-interruptible
interruptible
0
10
20
30
40
50
60
non-parametric
parametric
33.3 35.0
48.6 52.6
**：p < 0.01

20. 20
Residence time [s]
Ratioofspeakers[%]
0
10
20
30
40
50
20 40 60 80 100 100+
un-interruptible
interruptible
**
**：p < 0.01
Histogram of Residence Time

21. Subjective Evaluation
21
1
2
3
4
5
6
7
Non-Parametrization
Parametrization
Adjective
Score
non-parametric
parametric
• Analyze questionnaires of 42 subjects
• Evaluate by 7 levels in 28 opposite pairs of
adjectives

22. Factor Analysis by SD method
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-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
non-parametric
parametric
Friendliness
(cute, like,
friendly, ...)
Sensitivity
(active, fulfilling,
sensitive, ...)
Sophistication
(comprehensive,
wise, ...)
Speediness
(flashy, quick,
fast, ...)
+ ** ** **
Factorscore
**：p < 0.01
+：p < 0.1
People perceived wiser and more efficient

23. Conclusion
• Proposed HRI System
– Motion Graph + Rapidly-exploring Random Trees
– Match Web
– Key-Value Store + Episode Rules
• Evaluated HRI system
– Increase ratio of participants
– Increase residence time increase
– Obtain positive subjective evaluation
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24. Future Plan: HRI System
Supported by Cloud Database
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Learn motion by reinforcement learning
Semantic map Mental model from face/gaze

25. THANK YOU VERY MUCH
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