ATK: Automatic Task-driven Keypoint Selection for Robust Policy Learning
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Key Points
The ATK method enhances robustness to visual disturbances in robot policies, improving task performance.
Using a minimal set of predictive keypoints significantly boosts policy learning across different environments.
ATK focuses on task-relevant features, optimizing keypoint selection for improved policy transfer and robustness.
Validation on various robotic tasks shows improved adaptability and performance when facing environmental variations.
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Implication
This approach selects relevant keypoints to improve visual policy robustness in varied tasks, indicating significant advancements in imitation learning.