Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Planning Multimodal Exploratory Actions for Online Robot Attribute
  Learning   Robots frequently need to perceive object attributes, such as "red," "heavy,"
and "empty," using multimodal exploratory actions, such as "look," "lift," and
"shake." Robot attribute learning algorithms aim to learn an observation model
for each perceivable attribute given an exploratory action. Once the attribute
models are learned, they can be used to identify attributes of new objects,
answering questions, such as "Is this object red and empty?" Attribute learning
and identification are being treated as two separate problems in the
literature. In this paper, we first define a new problem called online robot
attribute learning (On-RAL), where the robot works on attribute learning and
attribute identification simultaneously. Then we develop an algorithm called
information-theoretic reward shaping (ITRS) that actively addresses the
trade-off between exploration and exploitation in On-RAL problems. ITRS was
compared with competitive robot attribute learning baselines, and experimental
results demonstrate ITRS' superiority in learning efficiency and identification
accuracy.

11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci