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Humanoid Robots Learning to Walk Faster: From the Real World to Simulation and Back ALON FARCHY, SAMUEL BARRETT, PATRICK MACALPINE, PETER STONE

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Motivation Low-level robot skills are important ◦Robust walking and turning ◦Precise robotic arm movement ◦Localization ◦Stability ◦Etc.

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Motivation Low-level robot skills are important ◦Robust walking and turning ◦Precise robotic arm movement ◦Localization ◦Stability ◦Etc. These skills can be parameterized, but learning on a robot is challenging: ◦Many environmental factors ◦Robot performance degrades with use ◦Robots take time to operate ◦Lack of ground truth

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A Little Background… RoboCup The RoboCup Standard Platform League: Soccer for robots Requires fast, stable, intelligent robots Robots wear out and are time consuming to work with Therefore, using machine learning is hard

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A Little Background… RoboCup Simulation The 3D Simulation League: Soccer for virtual robots Requires fast, stable, intelligent robots Robots are unlimited Great environment for machine learning In 2011 and 2012, UT Austin Villa Simulation League outpaced the competition by using machine learning.

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How can we transfer our knowledge to the real world? Transfer LearnApply

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Challenges Many differences between simulation and the real world: Open Dynamics Engine (ODE) is far from perfect No fiction on joints No heat simulation Virtual Nao is greatly approximated Equal joint strength, perfect balance, simple foot shape Soccer environment is greatly approximated Perfectly flat surface

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Outline Grounded Simulation Learning (GSL) ◦Assumptions, Parameters, Overview ◦Ground, Optimize, Guide Implementation ◦Fitness Evaluation ◦Predicting Joint Angles ◦Optimizing (CMA-ES) ◦Manual Guidance Results References

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Grounded Simulation Learning (GSL) Concept: Iteratively bound the search space to find areas that overlap between the simulation and the real word. Reduce disparity between simulation / real world along the way. Assumptions: 1. Evaluation in simulation can be modified. 2. A small number of evaluations can be run on the robot. 3. A small number of explorations can be run on the robot to collect data. 4. Using data from (3), the disparity between the simulation and robot can be reduced via supervised machine learning. 5. Optimization in simulation can be biased towards / against certain parameters.

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GSL: Parameters Input: P 0 – Initial parameter set Fitness sim – A simulation fitness function that uses a model that maps joint commands to outputs Fitness robot – a robot fitness function Explore robot – a robot exploration routine Learn – A supervised learning algorithm Optimize – An optimization algorithm to run in simulation Output: P opt – Optimized parameter set Variables: ◦BestFitness – Current best fitness evaluation on the robot ◦OpenParams – Bag of pairs (Parameter set, Fitness) to try

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GSL: Ground Using the next parameter set in OpenParams: 1. Collect data about the robots states and actions using Explore robot. 2. Use Learn to create a mapping between states and actions on the robot. 3. Use this mapping to reduce disparity between simulation and the real world. Force simulation to act like the robot

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GSL: Optimize Use Optimize to find good parameters in the grounded simulation. Note: The optimization should not search too deeply. Searching far from the base parameters is very likely to exploit idiosyncrasies in the simulation.

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GSL: Guide 1. Try some as many good parameters on the robot as is feasible. Add the good ones to OpenParams. 2. Based on results, select parameters to focus on for the next round of optimization. ◦In our case, this selection was performed manually. Repeat ground, optimize, and guide until OpenParams is empty.

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GSL: Putting it together P 0 – Initial parameter set Fitness sim – Simulation fitness function Fitness robot – Robot fitness function Explore robot – Robot exploration routine Learn – Supervised learning algorithm Optimize – Simulation optimization algorithm BestFitness – Current best fitness OpenParams – Bag (Parameter set, Fitness)

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GSL: Putting it together Explore (Robot) Learn Optimize (Sim) Evaluate (Robot) Guide OpenParams Good Params (robot) Pop States, Actions Model Good Params (sim) Good Params (robot) Focus

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Implementation Fitness Evaluation Predicting Joint Angles Optimizing (CMA-ES) Manual Guidance Results

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Fitness Evaluation (Real World) Walk 238cm forward (towards orange ball) Manual stop when foot reaches white line Robot measures time delta. Shorter time is better

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Fitness Evaluation (Real World)

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Fitness Evaluation (Simulation) Original: ◦OmniWalk (goToTarget) ◦Omnidirectional walk towards various targets. ◦Closer to target is better. ◦Penalty for falling. ◦Needs to be able to turn and stop quickly – out of scope. New: ◦WalkFront ◦Walk forward only for 15 seconds. ◦Measure forward delta. ◦Higher is better.

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Grounding: Predicting Joint Angles Explore robot : modified OmniWalk. ◦Only walk forward and turn ◦Record joint commands and joint angles at each frame Learn: M5P – Learn mapping from: ◦(Joint Angles, Joint Commands) to ◦Next Joint Angles RAE – Relative Absolute Error RRSE – Relative Root Squared Error

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Grounding: Predicting Joint Angles How to apply model to simulation? Linear combination of requested joint angles and predicted joint angles By manual testing, 70% requested / 30% predicted. Now we can use this grounded simulation in Optimize.

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Optimizing in Simulation: CMA-ES Covariance Matrix Adaptation Evolution Strategy ◦Candidates sampled from multidimentional Gaussian distribution. ◦Evaluated by Fitness sim ◦Weighted average of members with highest fitness used to update mean of distribution ◦Covariance updated using evolution paths controls search step sizes

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Optimizing in Simulation: CMA-ES

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Condor: workload management system. 150 simultaneous fitness evaluations. Even with small number of generations (~10), explores a LOT more parameter sets than a real robot could.

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Guidance Evaluate optimized parameters using Fitness robot. Select parameters for OpenParams (easy) ◦Robot Falls? ◦Robot Faster? Bias Optimize to better parameters (harder) ◦Manually tweaked variance of parameters in the CMA-ES. ◦Could be automated.

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Results GSL was run at 67% step size for stability. But ITER 4 (WalkFront) could run at 100% step size. 100%: 13.5 cm/s ◦http://youtu.be/grlceQkBTxw 100%: 17.1 cm/s ◦http://youtu.be/nGc127yYoSs 26.7% Improvement!

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Related Work UT Austin Villa RobotCup 3D Simulation League: P. MacAlpine, S. Barrett, D. Urieli, V. Vu, and P. Stone. Design and optimization of an omnidirectional humanoid walk: A winning approach at the RoboCup D simulation competition. In Twenty-Sixth Conference on Articial Intelligence (AAAI-12), July CMA-ES N. Hansen. The CMA evolution strategy: A tutorial, M5P R. J. Quinlan. Learning with continuous classes. In 5 th Australian Joint Conference on Articial Intelligence, pages 343{348, Singapore, World Scientific.

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Related Work Simulation + Robot learning: P. Abbeel, M. Quigley, and A. Y. Ng. Using inaccurate models in reinforcement learning. In International Conference on Machine Learning (ICML) Pittsburgh, pages 1{8. ACM Press, J. C. Zagal, J. Delpiano, and J. Ruiz-del Solar. Self-modeling in humanoid soccer robots. Robot. Auton. Syst., 57(8):819{827, July L. Iocchi, F. D. Libera, and E. Menegatti. Learning humanoid soccer actions interleaving simulated and real data. In Second Workshop on Humanoid Soccer Robots, November S. Koos, J.-B. Mouret, and S. Doncieux. Crossing the reality gap in evolutionary robotics by promoting transferable controllers. In Proceedings of the 12 th annual conference on Genetic and volutionary computation, GECCO '10, pages 119{126, New York, NY, USA, ACM.

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