Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Vision-Guided Dynamic Locomotion Over Challenging Terrains   Legged robots are becoming increasingly powerful and popular in recent years
for their potential to bring the mobility of autonomous agents to the next
level. This work presents a deep reinforcement learning approach that learns a
robust Lidar-based perceptual locomotion policy in a partially observable
environment using Proximal Policy Optimisation. Visual perception is critical
to actively overcome challenging terrains, and to do so, we propose a novel
learning strategy: Dynamic Reward Strategy (DRS), which serves as effective
heuristics to learn a versatile gait using a neural network architecture
without the need to access the history data. Moreover, in a modified version of
the OpenAI gym environment, the proposed work is evaluated with scores over 90%
success rate in all tested challenging terrains.

26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph