1. Introduction to RoboCup@Home
Imitation learning applied to domestic service robot tasks
2013/12/13
Komei Sugiura
National Institute of Information and Communication Technology, Japan
komei.sugiura@nict.go.jp
2. RoboCup@Home: Benchmark tests for domestic robots
• RoboCup@Home: The largest competition for domestic robots
– One of the major RoboCup leagues
– Focuses on human-robot interaction and mobile manipulation
– Robots are evaluated by 7 standardized and 3 demonstration tasks
• Info
– >200 participants from 15 countries
– 6-10 members/team
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4. Difficulties
• Mobile manipulation
– Navigation in unknown environments
– Surrounded by spectators
– Real shop environments
– Manipulation of everyday objects
• Human robot interaction
– Very noisy environments
– Robust dialogue management
– Gesture recognition
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5. Standard test 1: Cocktail Party
• Task: learn and recognize unknown persons, and deliver drinks
Item
Max score
Best team
Average
Detecting the calling persons
150 x 3
300
95
Understanding human/drink names
100 x 3
300
108
Delivering correct ordered drinks
200 x 3
400
31.6
5
6. Standard test 2: Restaurant
• Task: Retrieve three objects in an unknown environment e.g. restaurant
• Environment: a real restaurant (robots are transported)
Item
Score
Best team
Average
Reaching a location in the guide phase
50 x 5
250
143
Reaching a location in the navigation phase
100 x 4
200
50
Grasping the correct objects
250 x 3
500
45
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7. Standard test 3: Enduring General Purpose Service Robots
• NimbRo (Bonn University)
8. LCore Applied (1): Imitation learning for household activities
Teacher: “Throw-into.” ( with demonstrating motions several times)
Robot: (Estimates relative objects and learns motion trajectories)
User: “Throw a plastic bottle into a dust bin.”
Robot: (Searches for the objects and executes “throw-into” motion)
Dialog example
9. LCore Applied (2): Learning unknown words
• Difficulty: low phoneme recognition accuracy
• Proposed
– Learns phoneme sequence with waveform
– Voice conversion using EigenVoice Gaussian Mixture Model*[Toda+ 2007]
Evaluation using CMOS metric
• proposed method outperformed baseline
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