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Round 1: 2 months

NeurIPS 2019: Learn to Move - Walk Around

Reinforcement Learning on Musculoskeletal Models

1 Travel Grants
1 Authorship/Co-Authorship

Clarification:
- July 17: Evaluation environment for Round 1 will be set as model='3D', difficulty=2, project=True, and obs_as_dict=True

Page update:
- July 17: Competition Tracks and Prizes

Simulation update:
- June 28: observation_space defined
- June 15: action space reordered

Learn to Move: Walk Around

Welcome to the Learn to Move: Walk Around challenge, one of the official challenges in the NeurIPS 2019 Competition Track. Your task is to develop a controller for a physiologically plausible 3D human model to walk or run following velocity commands with minimum effort. You are provided with a human musculoskeletal model and a physics-based simulation environment, OpenSim. There will be three tracks: 1) Best performance, 2) Novel ML solution, and 3) Novel biomechanical solution, where all the winners of each track will be awarded.

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Competition Tracks and Prizes

  • Best Performance Track
    • Rank in the top 50 of Round 1
    • Get the highest score in Round 2
    • Prizes for the winner:
      • NVIDIA GPU
      • Travel grant (up to $1,000 to NeurIPS 2019 or Stanford)
  • ML (Machine Learning) Track (find more details here)
    • Rank in the top 50 of Round 1
    • Submit a paper describing your machine learning approach in NeurIPS format
    • We will select the best paper
    • Prizes for the winner:
      • 2x NVIDIA GPU
      • Travel grant (up to $1,000 to NeurIPS 2019 or Stanford)
      • Paper acceptance into the NeurIPS Deep RL workshop
  • NM (Neuromechanics) Track (find more details here)
  • Other support
    • Top 50 of Round 1: $500 Google Cloud credit
    • Top 200 (@July 31): $200 Google Cloud credit

Timeline

  • Round 1: June 6 ~ October 13, 2019
    • Get in top 50 to proceed to Round 2
    • Evaluation will be done with model=3D and difficulty=2
  • Round 2: October 14 ~ 27, 2019
    • Details TBA
  • Paper submission for Track 2 and 3: October 14 ~ 27, 2019
  • Winners announcement: November 22, 2019

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