Search Results for author: Massimo Vespignani

Found 1 papers, 0 papers with code

Deep Reinforcement Learning for Tensegrity Robot Locomotion

no code implementations28 Sep 2016 Marvin Zhang, Xinyang Geng, Jonathan Bruce, Ken Caluwaerts, Massimo Vespignani, Vytas SunSpiral, Pieter Abbeel, Sergey Levine

We evaluate our method with real-world and simulated experiments on the SUPERball tensegrity robot, showing that the learned policies generalize to changes in system parameters, unreliable sensor measurements, and variation in environmental conditions, including varied terrains and a range of different gravities.

reinforcement-learning

Cannot find the paper you are looking for? You can Submit a new open access paper.