NVIDIA’s New AI Cheated At Parkour…And Got Fixed!

By Two Minute Papers

AITechnologyScience
Share:

Key Concepts:

  • Motion capture data
  • Physics-based engine
  • Kinematic motions
  • Physically plausible motion
  • Dataset enrichment
  • Randomly generated levels
  • Path generation
  • Terrain variations
  • GPU training

1. Introduction:

  • The video discusses an AI player learning to navigate a challenging game environment, initially resembling a "parkour simulator."
  • The AI initially "cheats," but the process involves correcting these unrealistic motions.

2. Methodology: NVIDIA and Simon Fraser University's Three-Step Approach:

  • Step 1: Utilize Motion Capture Data: Start with a small dataset (14 minutes) of motion capture data from real humans.
  • Step 2: Generate Random Levels: Create new, randomly generated game levels.
  • Step 3: Physics-Based Motion Generation: Use a physics engine to generate new motions based on the existing data and the new levels. This corrects kinematic motions (AI-dreamed motions) that may involve floating or foot sliding.

3. The Iterative Training Process:

  • The AI generates new kinematic motions, which are then corrected by the physics engine to ensure physical plausibility.
  • These corrected motions are added to the initial dataset, enriching it.
  • The cycle repeats, continuously improving the AI's abilities.

4. Path Generation and Character Actions:

  • The AI generates paths within the levels that the character must follow.
  • These paths involve actions like climbing and jumping.

5. Results and Learning:

  • Initial training cycles yield imperfect results.
  • After three iterations of dataset enrichment with physics-based correction, the AI demonstrates significant improvement.
  • The AI learns to combine multiple motions, such as jumping, grabbing ledges, and climbing.
  • The AI can perform complex actions and navigate challenging environments.

6. Testing on New Environments:

  • The AI is tested on environments it has never seen before to assess its true intelligence.
  • The "green character" represents the AI's initial, uncorrected motion, while the "blue character" represents the physics-corrected motion.
  • The AI demonstrates the ability to adapt to new challenges, including climbing monuments and performing advanced maneuvers.
  • Example: The AI hops forward on one leg after a jump, showcasing natural and adaptive movement.

7. Data Enrichment and Training Requirements:

  • Each clip in the original motion capture dataset is converted into 50 different terrain variations.
  • The training process can be performed on a single, albeit high-end (A6000), GPU.
  • Training can take up to a month.

8. Limitations:

  • The motion generation process is slow, requiring approximately 25 seconds of GPU processing time to create 1 second of character movement.

9. Conclusion:

  • The research demonstrates the possibility of teaching an AI to navigate complex environments using a combination of motion capture data, physics-based simulation, and iterative dataset enrichment.
  • The technology has the potential to be implemented in games and virtual worlds.
  • The presenter expresses surprise that this work isn't more widely discussed.

Chat with this Video

AI-Powered

Hi! I can answer questions about this video "NVIDIA’s New AI Cheated At Parkour…And Got Fixed!". What would you like to know?

Chat is based on the transcript of this video and may not be 100% accurate.

Related Videos

Ready to summarize another video?

Summarize YouTube Video