Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm

Session WedGT3 Pegaso B Wednesday, October 10, 2012, 17:30–18:30
Home Automation and Personal Robots
Chair Stefano Mazzoleni, Scuola Superiore Sant'Anna
Co-Chair
17:30–17:45 WedGT3.1
Acquisition and Use of Transferable,
Spatio-Temporal Plan Representations for
Human-Robot Interaction
Michael Karg
Institute for Advanced Study, Technische Universität München, Germany
Alexandra Kirsch
Department of Computer Science, University of Tübingen, Germany
• Generation of semantically
annototated spatial model by
combining motion tracking data
with information from semantic
maps
• Automatic segmentation of
motion tracking data using
spatial model
• Generation of transferable,
general, spatio-temporal plan
representations for different
tasks
• Application: Passive plan
supervision in different
environments based on plan
patterns and durations at
semantically annotated locations
18:00–18:15 WedGT3.3
Context-aware Home Energy Saving based on
Energy-Prone Context
Mao-Yung Weng, Chao-Lin Wu, Ching-Hu Lu, Hui-Wen Yeh
and Li-Chen Fu
Department of Computer Science & Information Engineering,
National Taiwan University, Taiwan, R.O.C.
• Energy-prone context (EPC) is an
activity with associated energy
consumption.
• EPC contains information about the
necessity of an energy consumption to
an activity.
• We propose a systematic method to
determine energy saving (ES) service
based on EPC.
• The potential of EPC-based ES system
is 25% more effective than a locationbased
one.
An Energy-Prone Context
using WatchTV as an example
Activity: Watch TV
Location: Livingroom
Explicit:
TV_livingroom|on|120watt|1.0
Implicit:
AC_livingroom|on|3000watt|0.75
waterheater_bathroom|on|4000watt|
0.56
xbox_livingroom|standby|2watt|0.98
lamp_livingroom|off|0watt|0.96
light_hallway|off|0watt|0.88
light_kitchen|off|0watt|1.0
lamp_bedroom|off|0watt|0.86
light_bedroom|off|0watt|0.9
…..
Living Room Light:
on, 60w, 0.82
TV:
on, 120w, 1.0
A/C:
on, 3kw, 0.75
Watch
TV
Water Heater:
on, 4kw, 0.56
Explicit power consumption
Implicit power consumption
xBox:
standby, 2w, 0.98
17:45–18:00 WedGT3.2
Hierarchical Generalized Context Inference
for Context-aware Smart Homes
Chao-Lin Wu, Mao-Yuan Weng, Ching-Hu Lu and Li-Chen Fu
Department of Computer Science & Information Engineering,
National Taiwan University, Taiwan, R.O.C.
• Hierarchical generalized context
inference helps improve the
performance of multi-user
activity recognition.
• A generalized context (GC) is an
abstracted context composed of
several contexts with common
features.
• This mechanism treats multiple
users as an aggregated entity
and hierarchically group
contexts as GC.
• Context-aware smart homes
based on this method can
provide appropriate services as
much as possible.
Context Labels
Model Construction
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
–186–
GC(1)
C (1,
1)
C (i,
1)
Training
Phase
Hierarchical Context
Generalization
GC(0)
C (0, C (0, C (0, C(0, C (0,
1) 2) 3) 4)
…
j)
GC(i)
…
C (1,
2)
C(i,
2)
…
DBN DBN DBN
GC(0) GC(1)
C (1,
3)
…
…
…
GC(i)
C (1,
k)
…
C(i,
m)
Testing
Phase
Features
Generalized
Context
Inference
C(i,1) C(i,2) C(i,3) …
…
C(1,1
)
C(0,1
)
…
C(1,2
)
C(0,2
)
…
C(1,3
)
C(0,3
)
…
…
Generalized Contexts
Hierarchical Generalized Context Inference Engine
18:15–18:30 WedGT3.4
Complex Task Learning from
Unstructured Demonstrations
Unstructured Demonstrations
Scott Niekum, Sarah Osentoski, George Konidaris, and Andrew G. Barto
• We present a novel method for segmenting
demonstrations, recognizing repeated skills,
and generalizing complex tasks from
unstructured demonstrations.
• This method combines many of the
advantages of recent automatic segmentation
methods for learning from demonstration into
a single principled, integrated framework.
• Specifically, we use the Beta Process
Autoregressive Hidden Markov Model and
Dynamic Movement Primitives to learn and
generalize a multi-step task on the PR2
mobile manipulator and to demonstrate the
potential of our framework to learn a large
library of skills over time.