Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Imitation learning-based framework for learning 6-D linear compliant
  motions   We present a novel method for learning from demonstration 6-D tasks that can
be modeled as a sequence of linear motions and compliances. The focus of this
paper is the learning of a single linear primitive, many of which can be
sequenced to perform more complex tasks. The presented method learns from
demonstrations only, without any prior information, how to take advantage of
mechanical gradients in in-contact tasks, such as assembly, both for
translations and rotations. The method assumes there exists a desired linear
direction in 6-D which, if followed by the manipulator, leads the robot's
end-effector to the goal area shown in the demonstration, either in free space
or by leveraging contact through compliance. First, demonstrations are gathered
where the teacher explicitly shows the robot how the mechanical gradients can
be used as guidance towards the goal. From the demonstrations, a set of
directions is computed which would result in the observed motion at each
timestep during a demonstration of a single primitive. By observing which
direction is included in all these sets, we find a single desired direction
which can reproduce the demonstrated motion. Finding the number of compliant
axes and their directions in both rotation and translation is based on the
assumption that in the presence of a desired direction of motion, all other
observed motion is caused by the contact force of the environment, signalling
the need for compliance. We evaluate the method on a KUKA LWR4+ robot with test
setups imitating typical tasks where a human would use compliance to cope with
positional uncertainty. Results show that the method can successfully learn and
reproduce compliant motions by taking advantage of the geometry of the task,
therefore reducing the need for localization accuracy.

29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics