Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Credit Assignment Safety Learning from Human Demonstrations   A critical need in assistive robotics, such as assistive wheelchairs for
navigation, is a need to learn task intent and safety guarantees through user
interactions in order to ensure safe task performance. For tasks where the
objectives from the user are not easily defined, learning from user
demonstrations has been a key step in enabling learning. However, most robot
learning from demonstration (LfD) methods primarily rely on optimal
demonstration in order to successfully learn a control policy, which can be
challenging to acquire from novice users. Recent work does use suboptimal and
failed demonstrations to learn about task intent; few focus on learning safety
guarantees to prevent repeat failures experienced, essential for assistive
robots. Furthermore, interactive human-robot learning aims to minimize effort
from the human user to facilitate deployment in the real-world. As such,
requiring users to label the unsafe states or keyframes from the demonstrations
should not be a necessary requirement for learning. Here, we propose an
algorithm to learn a safety value function from a set of suboptimal and failed
demonstrations that is used to generate a real-time safety control filter.
Importantly, we develop a credit assignment method that extracts the failure
states from the failed demonstrations without requiring human labelling or
prespecified knowledge of unsafe regions. Furthermore, we extend our
formulation to allow for user-specific safety functions, by incorporating
user-defined safety rankings from which we can generate safety level sets
according to the users' preferences. By using both suboptimal and failed
demonstrations and the developed credit assignment formulation, we enable
learning a safety value function with minimal effort needed from the user,
making it more feasible for widespread use in human-robot interactive learning
tasks.

24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin