9 citations · 17 across the 20 of their papers we have counts for
7 papers · 1 filter
Learning Disturbances Online for Risk-Aware Control: Risk-Aware Flight with Less Than One Minute of Data
Prithvi Akella, Skylar X. Wei, Joel W. Burdick +1
Recent advances in safety-critical risk-aware control are predicated on apriori knowledge of the disturbances a system might face. This paper proposes a method to efficiently learn…
Safe Reinforcement Learning with Probabilistic Guarantees Satisfying Temporal Logic Specifications in Continuous Action Spaces
Hanna Krasowski, Prithvi Akella, Aaron D. Ames +1
Vanilla Reinforcement Learning (RL) can efficiently solve complex tasks but does not provide any guarantees on system behavior. To bridge this gap, we propose a three-step safe RL…
Safety-Critical Controller Verification via Sim2Real Gap Quantification
Prithvi Akella, Wyatt Ubellacker, Aaron D. Ames
The well-known quote from George Box states that: "All models are wrong, but some are useful." To develop more useful models, we quantify the inaccuracy with which a given model re…
Sample-Based Bounds for Coherent Risk Measures: Applications to Policy Synthesis and Verification
Prithvi Akella, Anushri Dixit, Mohamadreza Ahmadi +2
The dramatic increase of autonomous systems subject to variable environments has given rise to the pressing need to consider risk in both the synthesis and verification of policies…
A Scenario Approach to Risk-Aware Safety-Critical System Verification
Prithvi Akella, Mohamadreza Ahmadi, Aaron D. Ames
With the growing interest in deploying robots in unstructured and uncertain environments, there has been increasing interest in factoring risk into safety-critical control developm…
A Barrier-Based Scenario Approach to Verify Safety-Critical Systems
Prithvi Akella, Aaron D. Ames
In this letter, we detail our randomized approach to safety-critical system verification. Our method requires limited system data to make a strong verification statement. Specifica…