NLPサンプリング：多様な制約付きサンプリングのためのMCMCとNLP手法の融合
NLP Sampling: Combining MCMC and NLP Methods for Diverse Constrained Sampling
MCMC・制約付き最適化・ロボティクスを統合し、硬い制約下で多様なサンプルを生成するための問題定式化と再起動二段階法を提案し、ロボットマニピュレーション計画で評価した。
著者: Marc Toussaint, Cornelius V. Braun, Joaquim Ortiz-Haro
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Generating diverse samples under hard constraints is a core challenge in many areas. With this work we aim to provide an integrative view and framework to combine methods from the fields of MCMC, constrained optimization, as well as robotics, and gain insights in their strengths from empirical evaluations. We propose NLP Sampling as a general problem formulation, propose a family of restarting two-phase methods as a framework to integrated methods from across the fields, and evaluate them on analytical and robotic manipulation planning problems. Complementary to this, we provide several conceptual discussions, e.g. on the role of Lagrange parameters, global sampling, and the idea of a Diffused NLP and a corresponding model-based denoising sampler.
