Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning from the Hindsight Plan -- Episodic MPC Improvement   Model predictive control (MPC) is a popular control method that has proved
effective for robotics, among other fields. MPC performs re-planning at every
time step. Re-planning is done with a limited horizon per computational and
real-time constraints and often also for robustness to potential model errors.
However, the limited horizon leads to suboptimal performance. In this work, we
consider the iterative learning setting, where the same task can be repeated
several times, and propose a policy improvement scheme for MPC. The main idea
is that between executions we can, offline, run MPC with a longer horizon,
resulting in a hindsight plan. To bring the next real-world execution closer to
the hindsight plan, our approach learns to re-shape the original cost function
with the goal of satisfying the following property: short horizon planning (as
realistic during real executions) with respect to the shaped cost should result
in mimicking the hindsight plan. This effectively consolidates long-term
reasoning into the short-horizon planning. We empirically evaluate our approach
in contact-rich manipulation tasks both in simulated and real environments,
such as peg insertion by a real PR2 robot.

12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
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The categories of this paper are: nlin
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
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The categories of this paper are: econ
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The categories of this paper are: astro-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas