Imitation Learning uses the demonstrations of an expert to uncover the
optimal policy and it is suitable for real-world robotics tasks as well. In
this case, however, the training of the agent is carried out in a simulation
environment due to safety, economic and time constraints. Later, the agent is
applied in the real-life domain using sim-to-real methods. In this paper, we
apply Imitation Learning methods that solve a robotics task in a simulated
environment and use transfer learning to apply these solutions in the
real-world environment. Our task is set in the Duckietown environment, where
the robotic agent has to follow the right lane based on the input images of a
single forward-facing camera. We present three Imitation Learning and two
sim-to-real methods capable of achieving this task. A detailed comparison is
provided on these techniques to highlight their advantages and disadvantages.

本文研究应用 Imitation Learning 和 transfer learning 方法解决 Duckietown 场景下的机器人车道跟踪问题，并通过 sim-to-real 方法进行实际环境迁移，最终比较了三种 Imitation Learning 方法和两种 sim-to-real 方法的优缺点。

泛化自驾车策略的模仿学习及模拟转现实

Imitation Learning for Generalizable Self-driving Policy with  Sim-to-real Transfer

We have seen much recent progress in rigid object manipulation, but
interaction with deformable objects has notably lagged behind. Due to the large
configuration space of deformable objects, solutions using traditional
modelling approaches require significant engineering work. Perhaps then,
bypassing the need for explicit modelling and instead learning the control in
an end-to-end manner serves as a better approach? Despite the growing interest
in the use of end-to-end robot learning approaches, only a small amount of work
has focused on their applicability to deformable object manipulation. Moreover,
due to the large amount of data needed to learn these end-to-end solutions, an
emerging trend is to learn control policies in simulation and then transfer
them over to the real world. To-date, no work has explored whether it is
possible to learn and transfer deformable object policies. We believe that if
sim-to-real methods are to be employed further, then it should be possible to
learn to interact with a wide variety of objects, and not only rigid objects.
In this work, we use a combination of state-of-the-art deep reinforcement
learning algorithms to solve the problem of manipulating deformable objects
(specifically cloth). We evaluate our approach on three tasks --- folding a
towel up to a mark, folding a face towel diagonally, and draping a piece of
cloth over a hanger. Our agents are fully trained in simulation with domain
randomisation, and then successfully deployed in the real world without having
seen any real deformable objects.

采用最先进的深度强化学习算法，通过领域随机化，在仿真环境中训练代理程序以解决可变形物体操作问题，并成功将其部署在实际工作中。