Derives a log-determinant CIG reward from ensemble disagreement kernel with Cholesky factorization to produce causal per-step exploration signals that scale to high-dimensional spaces.
What is Intrinsic Motivation? A Typol- ogy of Computational Approaches
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QDTraj uses Quality-Diversity algorithms with sparse rewards to produce at least five times more diverse high-performing trajectories for articulated object manipulation than compared methods, validated across 30 objects with hundreds of trajectories per task.
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CIG: Exploration via Conditional Information Gain
Derives a log-determinant CIG reward from ensemble disagreement kernel with Cholesky factorization to produce causal per-step exploration signals that scale to high-dimensional spaces.
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QDTraj: Exploration of Diverse Trajectory Primitives for Articulated Objects Robotic Manipulation
QDTraj uses Quality-Diversity algorithms with sparse rewards to produce at least five times more diverse high-performing trajectories for articulated object manipulation than compared methods, validated across 30 objects with hundreds of trajectories per task.