Inrealisticscenarios with very limited numbers of data samples, it can be challenging to identify a kernel powerful enough to distinguish complex distributions.

Inthissection compare compare learned theprior environment 6. Experimental Tostudy proposed [54], where froman learning.

In CBF-based control, the desired safety properties of the system are mapped to nonnegativity of a CBF, and the control input is chosen to ensure that the CBF remains nonnegative for all time.

Thelossesarecompared onthreedegreesofshift(easy,moderate and hard), which is controlled by the drifted distance of Gaussian clusters. Herewediscuss the architecture chosen and the implementation details. Note that the task loss / surrogate loss function is used to update the meta-loss mϕ during meta-learning. The number of transformer layers and the hidden layers in MLP are selected from{1,2}. Wecanseethatthetask loss barely affects the learnt meta loss.

Compared toprevious works, our approach can learn landmarks that are more flexible in terms of capturing large changes in viewpoint.