Policy Gradient RL to learn fast walk

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1 Policy Gradient RL to learn fast walk Goal: Enable an Aibo to walk as fast as possible

2 Policy Gradient RL to learn fast walk Goal: Enable an Aibo to walk as fast as possible Start with a parameterized walk Learn fastest possible parameters

3 Policy Gradient RL to learn fast walk Goal: Enable an Aibo to walk as fast as possible Start with a parameterized walk Learn fastest possible parameters No simulator available: Learn entirely on robots Minimal human intervention

4 Walking Aibos Walks that come with Aibo are slow RoboCup soccer: 25+ Aibo teams internationally Motivates faster walks

5 Walking Aibos Walks that come with Aibo are slow RoboCup soccer: 25+ Aibo teams internationally Motivates faster walks Hand-tuned gaits [2003] Learned gaits German UT Austin Hornby et al. Kim & Uther Team Villa UNSW [1999] [2003] 230 mm/s (±5)

6 A Parameterized Walk Developed from scratch as part of UT Austin Villa 2003 Trot gait with elliptical locus on each leg

7 Locus Parameters z x y Ellipse length Ellipse height Position on x axis Position on y axis Body height Timing values 12 continuous parameters

8 Locus Parameters z x y Ellipse length Ellipse height Position on x axis Position on y axis Body height Timing values 12 continuous parameters Hand tuning by April, 03: 140 mm/s Hand tuning by July, 03: 245 mm/s

9 Experimental Setup Policy π = {θ 1,...,θ 12 }, V (π) = walk speed when using π

10 Experimental Setup Policy π = {θ 1,...,θ 12 }, V (π) = walk speed when using π Training Scenario Robots time themselves traversing fixed distance Multiple traversals (3) per policy to account for noise

11 Experimental Setup Policy π = {θ 1,...,θ 12 }, V (π) = walk speed when using π Training Scenario Robots time themselves traversing fixed distance Multiple traversals (3) per policy to account for noise Multiple robots evaluate policies simultaneously Off-board computer collects results, assigns policies

12 Experimental Setup Policy π = {θ 1,...,θ 12 }, V (π) = walk speed when using π Training Scenario Robots time themselves traversing fixed distance Multiple traversals (3) per policy to account for noise Multiple robots evaluate policies simultaneously Off-board computer collects results, assigns policies No human intervention except battery changes

13 Policy Gradient RL From π want to move in direction of gradient of V (π)

14 Policy Gradient RL From π want to move in direction of gradient of V (π) Can t compute V (π) θ i directly: estimate empirically

15 Policy Gradient RL From π want to move in direction of gradient of V (π) Can t compute V (π) θ i directly: estimate empirically Evaluate neighboring policies to estimate gradient Each trial randomly varies every parameter

16 Policy Gradient RL From π want to move in direction of gradient of V (π) Can t compute V (π) θ i directly: estimate empirically Evaluate neighboring policies to estimate gradient Each trial randomly varies every parameter

17 Experiments Started from stable, but fairly slow gait Used 3 robots simultaneously Each iteration takes 45 traversals, minutes

18 Experiments Started from stable, but fairly slow gait Used 3 robots simultaneously Each iteration takes 45 traversals, minutes Before learning After learning 24 iterations = 1080 field traversals, 3 hours

19 Results 300 Velocity of Learned Gait during Training Learned Gait (UT Austin Villa) 280 Velocity (mm/s) Learned Gait (UNSW) Hand tuned Gait (UNSW) Hand tuned Gait (UT Austin Villa) Hand tuned Gait (German Team) Number of Iterations

20 Results 300 Velocity of Learned Gait during Training Learned Gait (UT Austin Villa) 280 Velocity (mm/s) Learned Gait (UNSW) Hand tuned Gait (UNSW) Hand tuned Gait (UT Austin Villa) Hand tuned Gait (German Team) Number of Iterations Additional iterations didn t help Spikes: evaluation noise? large step size?

21 Learned Parameters Parameter Initial ǫ Best Value Value Front ellipse: (height) (x offset) (y offset) Rear ellipse: (height) (x offset) (y offset) Ellipse length Ellipse skew multiplier Front height Rear height Time to move through locus Time on ground

22 Algorithmic Comparison, Robot Port Before learning After learning

23 Summary Used policy gradient RL to learn fastest Aibo walk All learning done on real robots No human itervention (except battery changes)

24 Outline Learning sensor and action models [Stronger, S, 06] Machine learning for fast walking [Kohl, S, 04] Learning to acquire the ball [Fidelman, S, 06] Color constancy on mobile robots [Sridharan, S, 05] Autonomous Color Learning [Sridharan, S, 06]

25 Grasping the Ball Three stages: walk to ball; slow down; lower chin Head proprioception, IR chest sensor ball distance Movement specified by 4 parameters

26 Grasping the Ball Three stages: walk to ball; slow down; lower chin Head proprioception, IR chest sensor ball distance Movement specified by 4 parameters Brittle!

27 Parameterization slowdown dist: when to slow down slowdown factor: how much to slow down capture angle: when to stop turning capture dist: when to put down head

28 Learning the Chin Pinch Binary, noisy reinforcement signal: multiple trials Robot evaluates self: no human intervention

29 Results Evaluation of policy gradient, hill climbing, amoeba successful captures out of 100 trials policy gradient amoeba hill climbing iterations

30 What it learned Policy slowdown slowdown capture capture Success dist factor angle dist rate Initial 200mm o 110mm 36% Policy gradient 125mm o 152mm 64% Amoeba 208mm o 162mm 69% Hill climbing 240mm o 170mm 66%

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