Presented in the 2nd International Workshop on Cognitive Neuroscience Robotics.

1. Statistic Imitation Learning and
Human-Robot Communication
Komei Sugiura
NICT, Japan

2. Studies on imitation learning
Method References
DMP [Ijspeert 2002, Matsubara 2010] *Dynamic Motion Primitive
Neural networks RNNPB [Sugita 2005, Ogata 2007]
Probabilistic models • Gaussian processes [Lawrence 2004, Shon 2006]
• Gaussian Mixture regression [Calinon 2010]
• HMMs [Ogawara 2002, Inamura 2004, Billard 2006, Takano
2009, Taniguchi 2011]
Advantage of HMMs:
• Efficient algorithm for learning, recognition
and generation
• Input: Camera, mocap, direct teach, etc

3. Imitation learning of object manipulation [Sugiura+ 07]
• Difficulty: Clustering trajectories in the world coordinate system does not work
• Proposed method
– Input: Position sequences of all objects
– Estimation of reference point and coordinate system by EM algorithm
– Number of state is optimized by cross-validation
Place A on B

4. Imitation learning using reference-point-dependent HMMs
[Sugiura+ 07][Sugiura+ 11]
• Delta parameters
:Position at time t
= …
= …
Searching optimal coordinate system
Reference object ID
HMM
parameters
Coordinate system
type
* Sugiura, K. et al, “Learning, Recognition, and Generation of Motion by …”, Advanced Robotics, Vol.25, No.17, 2011

5. Results: motion learning
Place-on Move-closer Raise Rotate
Jump-over Move-away Move-down
Loglikelihood
Position
Velocity
Training-set likelihoodMotion “place A on B”
No verb is estimated to have WCS
-> Reference-point-dependent verb

6. Trajectory HMMs for imitating motion and speech
[Sugiura, IROS 2011]
“Place A on B” Motion
Speech
: State sequence
: HMM parameters
: Sequence of position, velocity &
acceleration
Maximum likelihood trajectory
: Matrix of OPDF’s covariance
matrices
: Vector of OPDF’s mean vectors
*Tokuda, K. et al, “Speech parameter generation algorithms for HMM-based speech synthesis”, 2000

7. Trajectory HMMs for imitating motion and speech
: State sequence
: HMM parameters
: Sequence of position, velocity &
acceleration
Maximum likelihood trajectory
: Matrix of OPDF’s covariance
matrices
: Vector of OPDF’s mean vectors
*Tokuda, K. et al, “Speech parameter generation algorithms for HMM-based speech synthesis”, 2000
: vector of mean vectors
: matrix of covariance
matrices of each OPDF
: filter ( )
: time series of position

8. Videos: Imitating motions
Place-on
Move-awayRotate

9. Demo:
Trajectory HMMs for Imitating Speech
9

10. Cloud-based TTS available without cost / authentication
• Send JSON command to server
{ “method” : “speak”,
"params" : [
“en",
“I’ll bring coke for you",
"*",
"audio/x-wav"
]}
{ “method” : “speak”,
"params" : [
"ja",
"こんにちは",
"*",
"audio/x-wav"
]}
http://rospeex.ucri.jgn-x.jp/nauth_json/jsServices/VoiceTraSS
Japanese
English
(Monologue)
Sample codes in JavaScript, Python, & C++ are available
Non-monologue speech synthesis Search

11. Results: Communication-oriented speech synthesis
• Trained with large-scale dataset (10 times larger than
conventional studies)
• Baseline << Proposed ≒ upper limit
Sugiura, K.et al, ICRA14
Non-monologue
AS B P1 P2 P3
(Upper limit)