Clemens Schwarke — accepted AI-conference papers

2025

Learning Deployable Locomotion Control via Differentiable Simulation

Clemens Schwarke, Victor Klemm, Joshua Bagajo, Jean Pierre Sleiman, Ignat Georgiev, Jesus Tordesillas Torres +1

CoRL 2025poster

Differentiable simulators promise to improve sample efficiency in robot learning by providing analytic gradients of the system dynamics. Yet, their application to contact-rich tasks like locomotion is complicated by the inherently non-smooth nature of contact, impeding effective gradient-based optim…

Cited by 0 Source ↗Scholar ↗

2023

Curiosity-Driven Learning of Joint Locomotion and Manipulation Tasks

Clemens Schwarke, Victor Klemm, Matthijs van der Boon, Marko Bjelonic, Marco Hutter

CoRL 2023poster

Learning complex locomotion and manipulation tasks presents significant challenges, often requiring extensive engineering of, e.g., reward functions or curricula to provide meaningful feedback to the Reinforcement Learning (RL) algorithm. This paper proposes an intrinsically motivated RL approach to…

Cited by 19 Source ↗Scholar ↗