activity
20102023
most citedEnd-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks

43 citations · 168 across the 35 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

cs.AI2021★ 1 cited

Risk-Averse Decision Making Under Uncertainty

Mohamadreza Ahmadi, Ugo Rosolia, Michel D. Ingham +2

A large class of decision making under uncertainty problems can be described via Markov decision processes (MDPs) or partially observable MDPs (POMDPs), with application to artific…

eess.SY2021★ 1 cited

Synthesis of Static Test Environments for Observing Sequence-like Behaviors in Autonomous Systems

Apurva Badithela, Richard M. Murray

In this paper, we investigate formal test-case generation for high-level mission objectives, specifically reachability, of autonomous systems. We use Kripke structures to represent…

eess.SY2021

Leveraging Classification Metrics for Quantitative System-Level Analysis with Temporal Logic Specifications

Apurva Badithela, Tichakorn Wongpiromsarn, Richard M. Murray

In many autonomy applications, performance of perception algorithms is important for effective planning and control. In this paper, we introduce a framework for computing the proba…

cs.RO2021★ 3 cited

Limits of Probabilistic Safety Guarantees when Considering Human Uncertainty

Richard Cheng, Richard M. Murray, Joel W. Burdick

When autonomous robots interact with humans, such as during autonomous driving, explicit safety guarantees are crucial in order to avoid potentially life-threatening accidents. Man…

eess.SY2021

Failure-Tolerant Contract-Based Design of an Automated Valet Parking System using a Directive-Response Architecture

Josefine Graebener, Tung Phan-Minh, Jiaqi Yan +2

Increased complexity in cyber-physical systems calls for modular system design methodologies that guarantee correct and reliable behavior, both in normal operations and in the pres…

eess.SY2021

Time-Optimal Navigation in Uncertain Environments with High-Level Specifications

Ugo Rosolia, Mohamadreza Ahmadi, Richard M. Murray +1

Mixed observable Markov decision processes (MOMDPs) are a modeling framework for autonomous systems described by both fully and partially observable states. In this work, we study…