no code implementations • 31 Mar 2022 • Steven Bohez, Saran Tunyasuvunakool, Philemon Brakel, Fereshteh Sadeghi, Leonard Hasenclever, Yuval Tassa, Emilio Parisotto, Jan Humplik, Tuomas Haarnoja, Roland Hafner, Markus Wulfmeier, Michael Neunert, Ben Moran, Noah Siegel, Andrea Huber, Francesco Romano, Nathan Batchelor, Federico Casarini, Josh Merel, Raia Hadsell, Nicolas Heess
We investigate the use of prior knowledge of human and animal movement to learn reusable locomotion skills for real legged robots.
An atmosphere-breathing electric propulsion system (ABEP) ingests the residual atmosphere particles through an intake and uses them as propellant for an electric thruster.
no code implementations • 1 Oct 2020 • Sabrina Livadiotti, Nicholas H. Crisp, Peter C. E. Roberts, Stephen D. Worrall, Vitor T. A. Oiko, Steve Edmondson, Sarah J. Haigh, Claire Huyton, Katharine L. Smith, Luciana A. Sinpetru, Brandon E. A. Holmes, Jonathan Becedas, Rosa María Domínguez, Valentín Cañas, Simon Christensen, Anders Mølgaard, Jens Nielsen, Morten Bisgaard, Yung-An Chan, Georg H. Herdrich, Francesco Romano, Stefanos Fasoulas, Constantin Traub, Daniel Garcia-Almiñana, Silvia Rodriguez-Donaire, Miquel Sureda, Dhiren Kataria, Badia Belkouchi, Alexis Conte, Jose Santiago Perez, Rachel Villain, Ron Outlaw
Limits and assets of each model have been discussed with regards to the context of this paper.
no code implementations • 2 Jan 2020 • Michael Neunert, Abbas Abdolmaleki, Markus Wulfmeier, Thomas Lampe, Jost Tobias Springenberg, Roland Hafner, Francesco Romano, Jonas Buchli, Nicolas Heess, Martin Riedmiller
In contrast, we propose to treat hybrid problems in their 'native' form by solving them with hybrid reinforcement learning, which optimizes for discrete and continuous actions simultaneously.
Learning robotic control policies in the real world gives rise to challenges in data efficiency, safety, and controlling the initial condition of the system.