Post on 13-Jan-2016
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Collaboration Development through Interactive Learning between Human and Robot
Tetsuya OGATA, Noritaka MASAGO, Shigeki SUGANO, Jun TANI.
Introduction
“Recent” studies about welfare robots or robots as pets attracted lots of attention
They must work flexibly and cooperatively with humans
They would also have a establish relations with people in daily life
The Aims
To demonstrate interactive learning between a human operator and a robot system
Both human and robot are in the role of the learner
But...These sorts of systems are usually difficult to
stabilize over long operation times
Previous Work
Most similar studies focus on short operations Exploring collapse and modification of
relationships between people
The Robot
Robovie 2 arms
4 DOF 'Human-like' head
Audiovisual sensors Many tactile sensors
attached to its body
The Environment
A 4x4m course The outside walls
marked alternately red and blue
The Experiment
The human and robot join arms and attempt to travel clockwise through the maze without hitting obstacles
Try to do it in the shortest time The movement is a
combination of the human's influence and the robot's neural network
Limited Senses
Both the human and robot have very limited sensory information
Robot has poor vision, and only local information such as ultrasonic sensors.
It has no global position information The human has a blindfold on
But can see the space before the experiment begins
Both sides are anticipating future sensory input and generating the next motor commands
The Model
A Recurrent Neural Network (RNN)
The input consists of: Current sensory input Current motor values
The output is predictions of: Next sensory input Next motor values
Their model can run in one of two modes
It can work in Open Loop Mode which directly maps inputs to outputs
Closed Loop Mode takes the output and puts it straight into the input
Can generate predictions of arbitrary length
Similar to mental rehearsal
Consolidation Learning
When a RNN tries to learn something new, it severely damages everything it already knows.
One way to avoid this: Save all past teaching data in a database Add new data Use all of the data to retrain
Learning time increases with data
Consolidation Learning
Analogous to biology Temporary memory stored in hippocampus Consolidated into long-term memory during sleep
New data is stored in a database The RNN corresponds to the long-term memory The RNN is trained using both the rehearsed
patterns and the sequence of the new experience This enables the incremental learning without
damaging the structure of the RNN
Navigation
In initial stages, performs very badly Has a collision avoidance system to help with
the training Simplified reinforcement learning for initial
training Robot and human go around workspace Time measured If performance is better, train RNN to
incorporate new trial
Experiments
A feed forward neural network (FFNN) A RNN A RNN with consolidation learning Trials interlaced with questionnaires meant to
judge workloads Effort, workload, complexity, performance,
concentration...
Results
FFNN ultimately deteriorates
RNN ultimately stagnates
RNN with consolidation learning continued to improve
Robustness
The analyze the effect of consolidation learning they compared the conventional RNN to the consolidation learning RNN when subject to noise
Used the closed loop mode and introduced different amounts of noise to the inputs
Consolidation learning proved far more robust Linked to 'operability' – Robots which don't
cope well with noise can seem unwieldy
Collaboration
Miwa showed that human collaboration was developed through repeated phases (Miwa et. al, 2001)
The consolidation-learning method arguably demonstrates these phases
The RNN with consolidation learning might have similarity with human learning