REVIEW 1 cited by
Approximate Robust Control of Uncertain Dynamical Systems
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
This work studies the design of safe control policies for large-scale non-linear systems operating in uncertain environments. In such a case, the robust control framework is a principled approach to safety that aims to maximize the worst-case performance of a system. However, the resulting optimization problem is generally intractable for non-linear systems with continuous states. To overcome this issue, we introduce two tractable methods that are based either on sampling or on a conservative approximation of the robust objective. The proposed approaches are applied to the problem of autonomous driving.
Forward citations
Cited by 1 Pith paper
-
DIAL: Distribution-Informed Adaptive Learning of Multi-Task Constraints for Safety-Critical Systems
DIAL learns a Beta-distributed safety constraint model from multi-task demonstrations and adapts it to new tasks via a tuned CVaR risk level, improving safety in RL transfer benchmarks.
Discussion (0). Continue with ORCID to comment.