Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cross-domain Imitation from Observations   Imitation learning seeks to circumvent the difficulty in designing proper
reward functions for training agents by utilizing expert behavior. With
environments modeled as Markov Decision Processes (MDP), most of the existing
imitation algorithms are contingent on the availability of expert
demonstrations in the same MDP as the one in which a new imitation policy is to
be learned. In this paper, we study the problem of how to imitate tasks when
there exist discrepancies between the expert and agent MDP. These discrepancies
across domains could include differing dynamics, viewpoint, or morphology; we
present a novel framework to learn correspondences across such domains.
Importantly, in contrast to prior works, we use unpaired and unaligned
trajectories containing only states in the expert domain, to learn this
correspondence. We utilize a cycle-consistency constraint on both the state
space and a domain agnostic latent space to do this. In addition, we enforce
consistency on the temporal position of states via a normalized position
estimator function, to align the trajectories across the two domains. Once this
correspondence is found, we can directly transfer the demonstrations on one
domain to the other and use it for imitation. Experiments across a wide variety
of challenging domains demonstrate the efficacy of our approach.

5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an