runbot
DESCRIPTION
Runbot. Team members: Marie Bro Duun Georgious Evangelos Emre Ozbilge Antonio Gomez Zamorano Matej Hoffmann Supervisor: Tao Geng. Goal. Make the Runbot robot learn to adjust step length Parameters: Maximum voltage to hip motors Extreme angle of hip joint - PowerPoint PPT PresentationTRANSCRIPT
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Runbot
Team members: Marie Bro Duun Georgious Evangelos Emre Ozbilge Antonio Gomez Zamorano Matej Hoffmann
Supervisor: Tao Geng
![Page 2: Runbot](https://reader033.vdocument.in/reader033/viewer/2022042822/568154e9550346895dc2dff4/html5/thumbnails/2.jpg)
Goal
Make the Runbot robot learn to adjust step length Parameters:
Maximum voltage to hip motors Extreme angle of hip joint
AEP – anterior extreme position
Emergency goal: make the robot walk without a touch sensor
![Page 3: Runbot](https://reader033.vdocument.in/reader033/viewer/2022042822/568154e9550346895dc2dff4/html5/thumbnails/3.jpg)
Relationship between parameters
Step length = f(max voltage, hip angle) ? A nontrivial nonlinear relationship Stability issue
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4 5 6 7 8 9 10 11
0
2
4
6
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20
Hip min angle 74
Column A
Hip max voltage
Ste
p le
ngth
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Optimization algorithms
'Heuristic' Evolutionary algorithms Simulated annealing
Gradient ascent methods Methods with memory – e.g. Q-learning
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Achievements
Real robot: Going to target step length from some initial
conditions
Simulation Optimization algorithm testbed – Simulink Several gradient based optimization methods tailored
to the problem
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Algorithm test bed
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![Page 10: Runbot](https://reader033.vdocument.in/reader033/viewer/2022042822/568154e9550346895dc2dff4/html5/thumbnails/10.jpg)
Pseudocode:
1) Short/Long Term Error Estimation
2) Relate Delta Constant to Estimated Error
3) Parameter selection by randomization
4) Parameter learning for Short/Long Term Gradient Policy Approach
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Open questions
Fitness landscape Can gradient be obtained reliably? Are there too many local minima? Fitness vs. stability Other control parameters? Step length vs. speed
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Next steps
Obtain a systematic rough picture of the fitness landscape from the real robot to assess feasibility of different optimization methods (e.g. gradient vs. non-gradient, methods with memory...)
Create a similar landscape in testbed and compare algorithms
Run experiments on real robot
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Thank you for your attention!