Risk-Aware Planning

Managing risk by stochastic submodular optimization

In addition to failures caused by DoS attacks, robots can fail randomly, which adds uncertainty to their performance. More broadly, the uncertainty comes from noisy robot sensing, imperfect robot motion, and unknown environmental conditions. The uncertainty usually puts robots’ performance at risk.

A standard strategy to deal with uncertainty is to optimize either the worst-case or the average of the stochastic performance. Both measures only consider a specific point of the distribution while not sufficiently utilizing the spread of the distribution. Instead, we utilized a risk measure, Conditional-Value-at-Risk (CVaR), commonly used for risk management in stocks portfolio optimization. CVaR is calculated based on the distribution of performance outcomes. By optimizing CVaR, the robots can manage the trustworthiness of their decision-making by tuning a risk parameter. For example, with a high risk level, the robots make more adventurous decisions to gain more rewards (one average) but with higher uncertainty. Instead, if robots choose a low risk level, they are more conservative and achieve fewer rewards but with lower uncertainty. To this end, we proposed the first polynomial-time algorithm that gives a bounded approximation for CVaR-based combinatorial optimization. This trustworthy algorithm has been used for enabling risk-aware multi-robot planning in environmental monitoring and mobility-on-demand, stochastic traveling salesman problem, and for handling uncertainty extractions from Bayesian deep learning models.

Along with optimizing CVaR for trustworthy decision-making, we designed a Pareto optimization scheme that adaptively balances maximizing team performance and minimizing risk of failures based on the abundance of heterogeneous team resources.

WAFR’18, T-RO: Risk-aware submodular optimization to deal with uncertainties for multi-robot coordination (left figure).
IROS’20: Risk-aware planning and assignment for ground vehicles using uncertain perception from aerial vehicles; dealing with uncertain extractions from Bayesian deep learning.
IROS’21: Risk-aware submodular optimization for stochastic traveling salesperson problem.
RA-L+ICRA’22: Adaptive and risk-aware target tracking for robot teams with heterogeneous sensors (right video).