73 citations · 117 across the 10 of their papers we have counts for
3 papers · 1 filter
DiffSim2Real: Deploying Quadrupedal Locomotion Policies Purely Trained in Differentiable Simulation
Joshua Bagajo, Clemens Schwarke, Victor Klemm +5
Differentiable simulators provide analytic gradients, enabling more sample-efficient learning algorithms and paving the way for data intensive learning tasks such as learning from…
Guided Reinforcement Learning for Robust Multi-Contact Loco-Manipulation
Jean-Pierre Sleiman, Mayank Mittal, Marco Hutter
Reinforcement learning (RL) often necessitates a meticulous Markov Decision Process (MDP) design tailored to each task. This work aims to address this challenge by proposing a syst…
Learning Deployable Locomotion Control via Differentiable Simulation
Clemens Schwarke, Victor Klemm, Joshua Bagajo +4
Differentiable simulators promise to improve sample efficiency in robot learning by providing analytic gradients of the system dynamics. Yet, their application to contact-rich task…