予測モデルを用いたロボットシステムのための安全指向ニューラルネットワーク修復
Repairing Neural Networks for Safety in Robotic Systems using Predictive Models
模倣学習で得た方策を予測モデルに基づいて修復し、安全制約を満たすロボット学習手法を提案。移動ロボットナビゲーションと下腿義足で有効性を示した。
著者: Keyvan Majd, Geoffrey Clark, Georgios Fainekos, Heni Ben Amor
分類: cs.RO
原文アブストラクト
This paper introduces a new method for safety-aware robot learning, focusing on repairing policies using predictive models. Our method combines behavioral cloning with neural network repair in a two-step supervised learning framework. It first learns a policy from expert demonstrations and then applies repair subject to predictive models to enforce safety constraints. The predictive models can encompass various aspects relevant to robot learning applications, such as proprioceptive states and collision likelihood. Our experimental results demonstrate that the learned policy successfully adheres to a predefined set of safety constraints on two applications: mobile robot navigation, and real-world lower-leg prostheses. Additionally, we have shown that our method effectively reduces repeated interaction with the robot, leading to substantial time savings during the learning process.
