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NIPS 2017: Learning to Run

Reinforcement learning environments with musculoskeletal models

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Overview

Welcome to Learning to Run, one of the 5 official challenges in the NIPS 2017 Competition Track In this competition, you are tasked with developing a controller to enable a physiologically-based human model to navigate a complex obstacle course as quickly as possible. You are provided with a human musculoskeletal model and a physics-based simulation environment where you can synthesize physically and physiologically accurate motion. Potential obstacles include external obstacles like steps, or a slippery floor, along with internal obstacles like muscle weakness or motor noise. You are scored based on the distance you travel through the obstacle course in a set amount of time.

Follow the instructions in the Getting Started guide in the Dataset section of the challenge and visit our github repo to get started!

2 steps

Partners

stanford epfl berkley stanford mobilize

Evaluation

Your task is to build a function f which takes the current state observation (a 41 dimensional vector) and returns the muscle excitations action (18 dimensional vector) in a way that maximizes the reward. Your total reward is the position of the pelvis on the x axis after the last iteration minus a penalty for using ligament forces. Ligaments are tissues which prevent your joints from bending too much - overusing these tissues leads to injuries, so we want to avoid it. The penalty in the total reward is equal to the sum of forces generated by ligaments over the trial, divided by 1000. For details on evaluation please refer to the Getting Started guide in the Dataset section of the challenge.

Rules

In order to avoid overfitting to the training environment, the top 10 participants will be asked to resubmit their solutions in the second round of the challenge. Environments in the second round will have the same structure but they will be initialized with different seeds. The final ranking will be based on results from the second round.

Additional rules:

  • You are not allowed to use external datasets (e.g., kinematics of people walking)
  • Organizers reserve the right to modify challenge rules as required.

Prizes

Prizes for top participants include:

  • Invitation to publish articles in the NIPS competition book.
  • Invitation to the 2nd Applied Machine Learning Days at EPFL in Switzerland on January 29 & 30, 2018, with travel and accommodation covered.
  • Invitation to give a research talk at Stanford, with travel and accommodation covered.
  • Reimbursement of travel and accommodation at NIPS 2017

Resources

Please refer to the Getting Started guide in the Dataset section of the challenge, for more details on how to access the challenge environments, and also for a basic tutorial on how to make your first submission.