Papers

Bidirectional Voronoi-biased Exploration Curriculum for Reinforcement Learning

This preprint introduces BVER, a bidirectional curriculum that grows start states from the goal and goals from the initial state distribution, biased toward unexplored task space and steered together RRT-style. On quadruped box climbing it reaches 95% success on a 0.4 m box in about 65% fewer iterations than the best reference-free baseline and is the only reference-free method tested to climb a 0.7 m box. It matters because it removes the need for demonstrations or hand-designed rewards in sparse-reward robot learning.