Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Online Object and Task Learning via Human Robot Interaction   This work describes the development of a robotic system that acquires
knowledge incrementally through human interaction where new tools and motions
are taught on the fly. The robotic system developed was one of the five
finalists in the KUKA Innovation Award competition and demonstrated during the
Hanover Messe 2018 in Germany. The main contributions of the system are a) a
novel incremental object learning module - a deep learning based localization
and recognition system - that allows a human to teach new objects to the robot,
b) an intuitive user interface for specifying 3D motion task associated with
the new object, c) a hybrid force-vision control module for performing
compliant motion on an unstructured surface. This paper describes the
implementation and integration of the main modules of the system and summarizes
the lessons learned from the competition.

27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
16
The categories of this paper are: stat
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin