  We introduce a risk-aware multi-objective Traveling Salesperson Problem (TSP)
variant, where the robot tour cost and tour reward have to be optimized
simultaneously. The robot obtains reward along the edges in the graph. We study
the case where the rewards and the costs exhibit diminishing marginal gains,
i.e., are submodular. Unlike prior work, we focus on the scenario where the
costs and the rewards are uncertain and seek to maximize the
Conditional-Value-at-Risk (CVaR) metric of the submodular function. We propose
a risk-aware greedy algorithm (RAGA) to find a bounded-approximation algorithm.
The approximation algorithm runs in polynomial time and is within a constant
factor of the optimal and an additive term that depends on the optimal
solution. We use the submodular function's curvature to improve approximation
results further and verify the algorithm's performance through simulations.
