Robotics
Preference-Augmented Distributional Reinforcement Learning for Risk-Sensitive Navigation
A learning approach for risk-sensitive navigation under uncertainty
Ensuring safe and reliable navigation under diverse sources of uncertainty remains a central challenge for autonomous systems.
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ArXiv CS.RO
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ArXiv CS.RO
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Autonomous systems must navigate safely despite diverse sources of uncertainty. The work’s focus on preferences and risk-sensitive learning addresses that challenge, though the supplied text does not report results.
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