Cat-like Jumping and Landing of Legged Robots in Low-gravity Using Deep Reinforcement Learning
This addresses locomotion challenges for legged robots in space exploration, but it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of controlling legged robots for jumping and landing in low-gravity environments, such as space exploration, by using deep reinforcement learning to train policies that enable repetitive, controlled movements with natural agility, demonstrated through sim-to-real transfer on the SpaceBok robot.
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.