Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Safety-Aware Preference-Based Learning for Safety-Critical Control   Bringing dynamic robots into the wild requires a tenuous balance between
performance and safety. Yet controllers designed to provide robust safety
guarantees often result in conservative behavior, and tuning these controllers
to find the ideal trade-off between performance and safety typically requires
domain expertise or a carefully constructed reward function. This work presents
a design paradigm for systematically achieving behaviors that balance
performance and robust safety by integrating safety-aware Preference-Based
Learning (PBL) with Control Barrier Functions (CBFs). Fusing these concepts --
safety-aware learning and safety-critical control -- gives a robust means to
achieve safe behaviors on complex robotic systems in practice. We demonstrate
the capability of this design paradigm to achieve safe and performant
perception-based autonomous operation of a quadrupedal robot both in simulation
and experimentally on hardware.

9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
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The categories of this paper are: funct-an
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The categories of this paper are: dg-ga
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The categories of this paper are: math
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
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The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
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The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-th
18
The categories of this paper are: econ