An Introduction to Trajectory Optimization: How to Do Your Own Direct Collocation

By Matthew Kelly. SIAM Review, 2017

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Vision-Only Robot Navigation in a Neural Radiance World (2021, background): “Of particular interest is the problem of trajectory optimization [20], which seeks a system trajectory x ( t ) and open-loop inputs u ( t ) that optimize a control objec-tive, subject to state and input constraints.”
Planning with Diffusion for Flexible Behavior Synthesis (2022, methodology): “Afterwards, the learned model may be plugged into classical trajectory optimization routines (Tassa et al., 2012; Posa et al., 2014; Kelly, 2017), which are similarly wellunderstood in their original context.”
Git Re-Basin: Merging Models modulo Permutation Symmetries (2022, background): “…does SGD thrive in optimizing high-dimensional non-convex deep learning loss land-scapes despite being noticeably less robust in other non-convex optimization settings, like policy learning (Ainsworth et al., 2021), trajectory optimization (Kelly, 2017), and recommender systems (Kang et al., 2016)?”