Ian Abraham

Robots can now find and return objects up to 19% faster by treating discovery as a Poisson process and optimizing the entire mission trajectory, including the return trip.

Sampling-Guided Policy Search (SGPS), which couples recurring action-target refinement by sampling-based model-predictive control with first-order policy optimization with first-order policy optimization, and shows that refinement improves policy learning beyond initialization and tracking alone.