Another limitation is that the linear model seems to outperform the rank-one quadratic model; we do not fully understand this effect, as discussed in the last paragraph of section 4. A third limitation is that models need to be averaged across time to obtain a single, deployable model: see Figure 5. A final limitation is that we do not yet have convergence theorems or regret bounds for the passive-aggressive updates in these models; see the second paragraph of section 5. (c) Did you discuss any potential negative societal impacts of your work?

At first, PLUMES will choose paths that randomly explore the environment. Each sample, however, provides new information about the targeted values in the surrounding environment -- such as spots with highest concentrations of chemicals or shallowest depths. The Gaussian process model exploits that data to narrow down possible paths the robot can follow from its given position to sample from locations with even higher value. PLUMES uses a novel objective function -- commonly used in machine-learning to maximize a reward -- to make the call of whether the robot should exploit past knowledge or explore the new area.