ガウス過程による制約学習と確率的制約付き動作計画
Gaussian Process Constraint Learning for Scalable Chance-Constrained Motion Planning from Demonstrations
局所最適なデモンストレーションからKKT条件を用いてガウス過程で制約を学習し、その不確実性を活かして確率的に安全な軌道を計画する手法を提案した。
著者: Glen Chou, Hao Wang, Dmitry Berenson
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
We propose a method for learning constraints represented as Gaussian processes (GPs) from locally-optimal demonstrations. Our approach uses the Karush-Kuhn-Tucker (KKT) optimality conditions to determine where on the demonstrations the constraint is tight, and a scaling of the constraint gradient at those states. We then train a GP representation of the constraint which is consistent with and which generalizes this information. We further show that the GP uncertainty can be used within a kinodynamic RRT to plan probabilistically-safe trajectories, and that we can exploit the GP structure within the planner to exactly achieve a specified safety probability. We demonstrate our method can learn complex, nonlinear constraints demonstrated on a 5D nonholonomic car, a 12D quadrotor, and a 3-link planar arm, all while requiring minimal prior information on the constraint. Our results suggest the learned GP constraint is accurate, outperforming previous constraint learning methods that require more a priori knowledge.
