An AI player is taught to survive in a challenging, parkour-like environment using motion capture data derived from real humans.
The initial motion capture dataset consists of only 14 minutes of material, which is limited for meaningful training.
The training method involves three steps: utilizing available human motion data, generating randomized new levels, and using a physics-based engine to create new, plausible motions on these levels.
The AI's kinematic motions (dreamed up by the AI) often contain physically implausible behaviors such as floating or foot sliding, which are considered "cheating."
A physics engine is used to correct these motions, making them physically believable.
Newly corrected movements are added to the original small dataset, progressively enlarging it across multiple cycles.
Characters are tasked with following generated paths that involve various parkour actions like climbing and jumping.
Cheating Detection and Iterative Improvement 02:06
Early attempts at motion enrichment result in the AI taking non-physical shortcuts (cheating) that are subsequently corrected through physics-based filtering.
After three cycles of data enrichment and correction, the AI demonstrates significantly more realistic and complex movement abilities.
The AI learns to combine multiple movements (e.g., jumping, grabbing, and climbing up edges) in novel ways not present in the original dataset.
Its abilities are tested on environments it has never seen before, demonstrating strong generalization.
The green character represents uncorrected AI motions, while the blue character has undergone physics-based correction and is more physically plausible.
The AI performs advanced maneuvers, such as sequential jumps and even naturally hopping on one leg without hesitating, producing realistic and fluid behavior.
The AI effectively handles challenging new levels, like climbing complex monuments and spirals.