TY - JOUR
AU - Hutter, Marco
AB - Abstract: In this article, we show that learned policies can be applied to solve legged locomotion control tasks with extensive flight phases, such as those encountered in space exploration. Using an off-the-shelf deep reinforcement learning algorithm, we trained a neural network to control a jumping quadruped robot while solely using its limbs for attitude control. We present tasks of increasing complexity leading to a combination of three-dimensional (re-)orientation and landing locomotion behaviors of a quadruped robot traversing simulated low-gravity celestial bodies. We show that our approach easily generalizes across these tasks and successfully trains policies for each case. Using sim-to-real transfer, we deploy trained policies in the real world on the SpaceBok robot placed on an experimental testbed designed for two-dimensional micro-gravity experiments. The experimental results demonstrate that repetitive, controlled jumping and landing with natural agility is possible.
TI - Cat-like Jumping and Landing of Legged Robots in Low-gravity Using Deep Reinforcement Learning
JF - Computing Research Repository
DO - 10.1109/TRO.2021.3084374
DA - 2021-06-17
UR - https://www.deepdyve.com/lp/arxiv-cornell-university/cat-like-jumping-and-landing-of-legged-robots-in-low-gravity-using-i46TB0MDTo
VL - 2021
IS - 2106
DP - DeepDyve
ER -