安全に探索できる状態空間：階層的タスク最適化による方策探索の安全な探索
Safe-To-Explore State Spaces: Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization
把持などのタスクを優先度付きのサブタスクに分解し、上位タスクの零空間内でのみ下位タスクの方策を学習することで、衝突を避けつつ安全かつ効率的にロボットのスキルを獲得する手法を提案した。
著者: Jens Lundell, Robert Krug, Erik Schaffernicht, Todor Stoyanov, Ville Kyrki
分類: cs.RO
原文アブストラクト
Policy search reinforcement learning allows robots to acquire skills by themselves. However, the learning procedure is inherently unsafe as the robot has no a-priori way to predict the consequences of the exploratory actions it takes. Therefore, exploration can lead to collisions with the potential to harm the robot and/or the environment. In this work we address the safety aspect by constraining the exploration to happen in safe-to-explore state spaces. These are formed by decomposing target skills (e.g., grasping) into higher ranked sub-tasks (e.g., collision avoidance, joint limit avoidance) and lower ranked movement tasks (e.g., reaching). Sub-tasks are defined as concurrent controllers (policies) in different operational spaces together with associated Jacobians representing their joint-space mapping. Safety is ensured by only learning policies corresponding to lower ranked sub-tasks in the redundant null space of higher ranked ones. As a side benefit, learning in sub-manifolds of the state-space also facilitates sample efficiency. Reaching skills performed in simulation and grasping skills performed on a real robot validate the usefulness of the proposed approach.
