TamedPUMA: 幾何学的ファブリックによる安全で安定した模倣学習
TamedPUMA: safe and stable imitation learning with geometric fabrics
模倣学習に幾何学的ファブリックを組み合わせ、衝突回避や関節制限などの制約を満たしつつ安定した動作生成を実現する手法を提案し、7自由度マニピュレータで実証した。
著者: Saray Bakker, Rodrigo Pérez-Dattari, Cosimo Della Santina, Wendelin Böhmer, Javier Alonso-Mora
分類: eess.SY, cs.LG, cs.RO, cs.SY
原文アブストラクト
Using the language of dynamical systems, Imitation learning (IL) provides an intuitive and effective way of teaching stable task-space motions to robots with goal convergence. Yet, IL techniques are affected by serious limitations when it comes to ensuring safety and fulfillment of physical constraints. With this work, we solve this challenge via TamedPUMA, an IL algorithm augmented with a recent development in motion generation called geometric fabrics. As both the IL policy and geometric fabrics describe motions as artificial second-order dynamical systems, we propose two variations where IL provides a navigation policy for geometric fabrics. The result is a stable imitation learning strategy within which we can seamlessly blend geometrical constraints like collision avoidance and joint limits. Beyond providing a theoretical analysis, we demonstrate TamedPUMA with simulated and real-world tasks, including a 7-DoF manipulator.
