5 papers indexed
Alex Brandes, Haig Conti Georges Sajelian, Manthan Patel, Dominik Hollidt, Chenhao Li, Matthias Heyrman, et al.
Deploying humanoid robots in unstructured terrain remains an open problem. While classic reinforcement learning struggles with the sheer complexity of real-world interactions, more promising methods leveraging human priors remain limited to models lacking contextual awareness. Th…
Shuyang Shi, Kaixian Qu, Changan Chen, Ines Kast, Yuntao Ma, Marco Hutter
Global navigation for ground robots in complex multi-level environments requires representations that accurately capture traversable regions while enabling efficient path planning. Current approaches present key limitations: Point clouds and volumetric occupancy maps lack explici…
Yixiao Li, Tifanny Portela, Jordis Herrmann, René Zurbrügg, Marco Hutter
Neural Motion Planners (NMPs) enable fast reactive motion generation, but adapting them to new environments typically requires recollecting large expert datasets, which is computationally prohibitive. We propose ELMP, a framework for data-efficient adaptation via self-supervised…
Jiale Fan, Connor Flynn, Tianao Xu, Junzhe He, Andrei Cramariuc, Marco Hutter, et al.
Learning-based control has revolutionized dynamic locomotion, yet navigating unstructured terrain remains limited by a robot's incomplete awareness of imminent ground contact. While global perception systems such as LiDARs and depth cameras provide environmental context, they are…
Ethan Marot, Thomas Bi, Clemens Schwarke, Victor Klemm, Marco Hutter, Raffaello D'Andrea
As humanoid robots become increasingly dynamic, coupling them with reinforcement learning offers a promising approach to solving the complex, underactuated mechanics of passive inline skating. Equipping a humanoid robot with passive inline skating wheels presents an opportunity t…