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Sparse graph policies not only outperform traditional image-based methods but also expose hidden dataset biases, enhancing both performance and interpretability in robot learning.
SPARC reduces noisy labels by leveraging task structure, enabling robots to learn from more reliable demonstrations and outperforming traditional methods in real-world applications.
