実演からのコストと制約の同時学習による安全な軌道生成
Jointly Learning Cost and Constraints from Demonstrations for Safe Trajectory Generation
実演データからコスト関数と未知の制約を分離して推定する二段階最適化手法を提案し、シミュレーションと実機マニピュレーションで有効性を示した。
著者: Shivam Chaubey, Francesco Verdoja, Ville Kyrki
分類: cs.RO
原文アブストラクト
Learning from Demonstration allows robots to mimic human actions. However, these methods do not model constraints crucial to ensure safety of the learned skill. Moreover, even when explicitly modelling constraints, they rely on the assumption of a known cost function, which limits their practical usability for task with unknown cost. In this work we propose a two-step optimization process that allow to estimate cost and constraints by decoupling the learning of cost functions from the identification of unknown constraints within the demonstrated trajectories. Initially, we identify the cost function by isolating the effect of constraints on parts of the demonstrations. Subsequently, a constraint leaning method is used to identify the unknown constraints. Our approach is validated both on simulated trajectories and a real robotic manipulation task. Our experiments show the impact that incorrect cost estimation has on the learned constraints and illustrate how the proposed method is able to infer unknown constraints, such as obstacles, from demonstrated trajectories without any initial knowledge of the cost.
