Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Causal Imitative Model for Autonomous Driving   Imitation learning is a powerful approach for learning autonomous driving
policy by leveraging data from expert driver demonstrations. However, driving
policies trained via imitation learning that neglect the causal structure of
expert demonstrations yield two undesirable behaviors: inertia and collision.
In this paper, we propose Causal Imitative Model (CIM) to address inertia and
collision problems. CIM explicitly discovers the causal model and utilizes it
to train the policy. Specifically, CIM disentangles the input to a set of
latent variables, selects the causal variables, and determines the next
position by leveraging the selected variables. Our experiments show that our
method outperforms previous work in terms of inertia and collision rates.
Moreover, thanks to exploiting the causal structure, CIM shrinks the input
dimension to only two, hence, can adapt to new environments in a few-shot
setting. Code is available at https://github.com/vita-epfl/CIM.

0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th