用预测模型修复神经网络，让机器人学习更安全高效
Repairing Neural Networks for Safety in Robotic Systems using Predictive Models
- 先用专家数据学策略，再用预测模型修复以保证安全
- 在导航和假肢任务中均满足预设安全约束
- 减少与机器人反复交互，大幅节省训练时间
本文提出一种面向机器人系统的安全感知学习方法，通过预测模型修复神经网络策略。该方法采用两阶段监督学习：首先从专家示范中学习策略，然后基于预测模型进行修复，以强制执行安全约束。预测模型可涵盖本体感觉状态、碰撞概率等关键因素。实验表明，该方法在移动机器人导航和真实下肢假肢应用中均成功满足预设安全约束；同时显著减少与机器人反复交互的次数，大幅节省学习过程时间。
原文摘要 · Abstract (English)
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.
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