用带参数偏置的随机预测网络，让机器人自适应环境并减少运动波动。
Environmentally Adaptive Control Including Variance Minimization Using Stochastic Predictive Network with Parametric Bias: Application to Mobile Robots
- 构建含参数偏置与随机项的循环神经网络作为预测模型。
- 在仿真和真实机械臂Fetch上验证，显著降低运动方差。
- 适合柔性或观测不全的移动机器人，尤其需稳定控制场景。
本文提出一种包含参数偏置和随机元素的递归神经网络构成的预测模型，以及基于该模型的环境自适应机器人控制方法，实现方差最小化。对于具有柔性本体或状态仅部分可观测的机器人，其动态模型常具随机性，且物理状态与环境随时间变化，导致预测模型需在线更新。为此，本文设计了一种基于经验学习的、嵌入环境信息的神经网络预测模型，并开发了可自适应当前环境、抑制大方差不稳定运动的控制策略。该方法在仿真环境及真实机器人Fetch上进行了验证。
原文摘要 · Abstract (English)
In this study, we propose a predictive model composed of a recurrent neural network including parametric bias and stochastic elements, and an environmentally adaptive robot control method including variance minimization using the model. Robots which have flexible bodies or whose states can only be partially observed are difficult to modelize, and their predictive models often have stochastic behaviors. In addition, the physical state of the robot and the surrounding environment change sequentially, and so the predictive model can change online. Therefore, in this study, we construct a learning-based stochastic predictive model implemented in a neural network embedded with such information from the experience of the robot, and develop a control method for the robot to avoid unstable motion with large variance while adapting to the current environment. This method is verified through a mobile robot in simulation and to the actual robot Fetch.
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