Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Navigation Costs from Demonstration with Semantic Observations   This paper focuses on inverse reinforcement learning (IRL) for autonomous
robot navigation using semantic observations. The objective is to infer a cost
function that explains demonstrated behavior while relying only on the expert's
observations and state-control trajectory. We develop a map encoder, which
infers semantic class probabilities from the observation sequence, and a cost
encoder, defined as deep neural network over the semantic features. Since the
expert cost is not directly observable, the representation parameters can only
be optimized by differentiating the error between demonstrated controls and a
control policy computed from the cost estimate. The error is optimized using a
closed-form subgradient computed only over a subset of promising states via a
motion planning algorithm. We show that our approach learns to follow traffic
rules in the autonomous driving CARLA simulator by relying on semantic
observations of cars, sidewalks and road lanes.

8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph