Robot Learning

Optimization Landscape

An optimization landscape is the surface a loss or objective function defines over parameter space, whose geometry, including local minima, saddle points, flat regions, and sharp valleys, governs how difficult optimization is. In robotics the term applies to neural network training as well as trajectory optimization and policy search, where contact discontinuities famously produce rugged, gradient-hostile landscapes.

Why it matters for physical AI

Contact-rich dynamics create nonsmooth objectives that defeat naive gradient methods, motivating smoothing techniques, sampling-based optimizers, and reinforcement learning formulations throughout physical AI.