3 accepted papers
Motion trajectories offer reliable references for physics-based motion learning but suffer from sparsity, particularly in regions that lack sufficient data coverage. To address this challenge, we introduce a self-supervised, structured representation and generation method that extracts spatial-tempo…
This work explores the potential of using differentiable simulation for learning robot control. Differentiable simulation promises fast convergence and stable training by computing low-variance first-order gradients using the robot model. Still, so far, its usage for legged robots is limited to simu…
Today's robotic quadruped systems can robustly walk over a diverse range of rough but continuous terrains, where the terrain elevation varies gradually. Locomotion on discontinuous terrains, such as those with gaps or obstacles, presents a complementary set of challenges. In discontinuous settings,…