Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Incremental Adversarial Learning for Optimal Path Planning   Path planning plays an essential role in many areas of robotics. Various
planning techniques have been presented, either focusing on learning a specific
task from demonstrations or retrieving trajectories by optimizing for
hand-crafted cost functions which are well defined a priori. In this work, we
present an incremental adversarial learning-based framework that allows
inferring implicit behaviour, i.e. the natural characteristic of a set of given
trajectories. To achieve adversarial learning, a zero-sum game is constructed
between a planning algorithm and an adversary - the discriminator. We employ
the discriminator within an optimal motion planning algorithm, such that costs
can be learned and optimized iteratively, improving the integration of implicit
behavior. By combining a cost-based planning approach with trained intrinsic
behaviour, this can be be integrated also with other constraints such as
obstacles or general cost factors within a single planning framework. We
demonstrate the proposed method on a dataset for collision avoidance, as well
as for the generation of human-like trajectories from motion capture data. Our
results show that incremental adversarial learning is able to generate paths
that reflect the natural implicit behaviour of a dataset, with the ability to
improve on performance using iterative learning and generation.

2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess