SOLAR-GP: Sparse Online Locally Adaptive Regression Using Gaussian Processes for Bayesian Robot Model Learning and Control
SOLAR-GP: Sparse Online Locally Adaptive Regression Using Gaussian Processes for Bayesian Robot Model Learning and Control
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DOI：
10.1109/lra.2020.2974432
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发表时间：
2020-02
影响因子：
5.2
通讯作者：
B. Wilcox;Michael C. Yip
中科院分区：
文献类型：
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作者：
B. Wilcox;Michael C. Yip
机器学习方法已经广泛用于机器人控制中以学习逆映射。这些方法用于捕获系统的整个非线性和非理想性，这些非线性和非理想性使得几何或现象学建模变得困难。大多数方法采用某种形式的离线或批量学习，其中训练可以在任务之前执行，或者分别以间歇的方式执行。这些策略通常不适用于远程操作，其中命令和传感器数据以顺序流接收，并且模型必须在运行中学习。我们结合联合收割机稀疏，本地和流式方法，形成稀疏在线局部自适应回归高斯过程（SOLAR-GP），训练流数据本地稀疏高斯过程模型和推断加权局部函数映射的机器人传感器状态关节状态。所得到的预测的遥操作命令用于联合控制。该算法适用于执行任意链接机械手，包括巴克斯特机器人，其中修改的算法进行并行运行的训练和预测，以保持一致，高频控制回路速率。该框架允许对生成的局部模型的复杂性进行用户定义的上限，同时保留所探索的状态空间的旧区域的信息。
Machine learning methods have been widely used in robot control to learn inverse mappings. These methods are used to capture the entire non-linearities and non-idealities of a system that make geometric or phenomenological modeling difficult. Most methods employ some form of off-line or batch learning where training may be performed prior to a task, or in an intermittent manner, respectively. These strategies are generally unsuitable for teleoperation, where commands and sensor data are received in sequential streams and models must be learned on-the-fly. We combine sparse, local, and streaming methods to form Sparse Online Locally Adaptive Regression using Gaussian Processes (SOLAR–GP), which trains streaming data on localized sparse Gaussian Process models and infers a weighted local function mapping of the robot sensor states to joint states. The resultant prediction of the teleoperation command is used for joint control. The algorithm was adapted to perform on arbitrary link manipulators including the Baxter robot, where modifications to the algorithm are made to run training and prediction in parallel so as to keep consistent, high-frequency control loop rates. This framework allows for a user-defined cap on complexity of generated local models while retaining information on older regions of the explored state-space.