  Despite decades of research, existing navigation systems still face
real-world challenges when deployed in the wild, e.g., in cluttered home
environments or in human-occupied public spaces. To address this, we present a
new class of implicit control policies combining the benefits of imitation
learning with the robust handling of system constraints from Model Predictive
Control (MPC). Our approach, called Performer-MPC, uses a learned cost function
parameterized by vision context embeddings provided by Performers -- a low-rank
implicit-attention Transformer. We jointly train the cost function and
construct the controller relying on it, effectively solving end-to-end the
corresponding bi-level optimization problem. We show that the resulting policy
improves standard MPC performance by leveraging a few expert demonstrations of
the desired navigation behavior in different challenging real-world scenarios.
Compared with a standard MPC policy, Performer-MPC achieves >40% better goal
reached in cluttered environments and >65% better on social metrics when
navigating around humans.
