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Found 14 papers, 0 papers with code
Sample-Based Bounds for Coherent Risk Measures: Applications to Policy Synthesis and Verification
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 for these systems.
Risk-Averse Receding Horizon Motion Planning for Obstacle Avoidance using Coherent Risk Measures
This paper studies the problem of risk-averse receding horizon motion planning for agents with uncertain dynamics, in the presence of stochastic, dynamic obstacles.
Safe Control for Nonlinear Systems with Stochastic Uncertainty via Risk Control Barrier Functions
To this end, we introduce risk control barrier functions (RCBFs), which are compositions of barrier functions and dynamic, coherent risk measures.
Distributionally Robust Model Predictive Control with Total Variation Distance
This paper studies the problem of distributionally robust model predictive control (MPC) using total variation distance ambiguity sets.
A Scenario Approach to Risk-Aware Safety-Critical System Verification
With the growing interest in deploying robots in unstructured and uncertain environments, there has been increasing interest in factoring risk into safety-critical control development.
Risk-Averse Decision Making Under Uncertainty
In this paper, we consider the problem of designing policies for MDPs and POMDPs with objectives and constraints in terms of dynamic coherent risk measures, which we refer to as the constrained risk-averse problem.
Risk-Averse Stochastic Shortest Path Planning
We consider the stochastic shortest path planning problem in MDPs, i. e., the problem of designing policies that ensure reaching a goal state from a given initial state with minimum accrued cost.
Constrained Risk-Averse Markov Decision Processes
We consider the problem of designing policies for Markov decision processes (MDPs) with dynamic coherent risk objectives and constraints.
Risk-Sensitive Motion Planning using Entropic Value-at-Risk
We consider the problem of risk-sensitive motion planning in the presence of randomly moving obstacles.
Constrained Active Classification Using Partially Observable Markov Decision Processes
The proposed framework relies on assigning a classification belief (a probability distribution) to the attributes of interest.
Partially Observable Games for Secure Autonomy
Technology development efforts in autonomy and cyber-defense have been evolving independently of each other, over the past decade.
Stochastic Finite State Control of POMDPs with LTL Specifications
Partially observable Markov decision processes (POMDPs) provide a modeling framework for autonomous decision making under uncertainty and imperfect sensing, e. g. robot manipulation and self-driving cars.
Risk-Averse Planning Under Uncertainty
We consider the problem of designing policies for partially observable Markov decision processes (POMDPs) with dynamic coherent risk objectives.
The Partially Observable Games We Play for Cyber Deception
Then, the deception problem is to compute a strategy for the deceiver that minimizes the expected cost of deception against all strategies of the infiltrator.
