Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cat-like Jumping and Landing of Legged Robots in Low-gravity Using Deep
  Reinforcement Learning   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.

26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc