Conditional Neural Movement Primitives
Conditional Neural Movement Primitives
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条件神经运动原语
DOI：
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发表时间：
2019
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影响因子：
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通讯作者：
Emre Ugur
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作者：
M. Seker;Mert Imre;J. Piater;Emre Ugur
条件神经运动原语 (CNMP) 是一种从演示框架中学习的框架，该框架被设计为建立在最新的深度神经架构（即条件神经过程 (CNP)）之上的机器人运动学习和生成系统。 CNMP 基于 CNP，通过对训练数据进行采样观察，直接从训练数据中提取先验知识，并使用它来预测任何其他目标点的条件分布。 CNMP 专门学习与外部参数和目标相关的复杂时间多模态感觉运动关系；在关节或任务空间中产生运动轨迹；并通过高级反馈控制环路执行这些轨迹。以机器人感觉运动空间中编码的外部目标为条件，CNMP 生成在成功执行任务期间预期观察到的预测感觉运动轨迹，并执行相应的运动命令。为了在操作执行期间检测意外事件并做出反应，CNMP 进一步根据每个时间步中的实际传感器读数进行调节。通过模拟和真实的机器人实验，我们表明 CNMP 可以从少量的演示中学习低维参数空间和复杂运动轨迹之间的非线性关系；他们还可以通过大量演示来模拟高维感觉运动空间和复杂运动之间的关联。实验进一步表明，即使没有明确向系统提供任务参数，机器人也可以通过将学习到的感觉运动表征与运动轨迹相关联来学习它们的影响。例如，机器人通过利用其感觉运动空间（包括本体感觉和力测量）来了解物体重量和形状的影响；当这些因素之一通过外部干预改变时，能够动态改变运动轨迹。
Conditional Neural Movement Primitives (CNMPs) is a learning from demonstration framework that is designed as a robotic movement learning and generation system built on top of a recent deep neural architecture, namely Conditional Neural Processes (CNPs). Based on CNPs, CNMPs extract the prior knowledge directly from the training data by sampling observations from it, and uses it to predict a conditional distribution over any other target points. CNMPs specifically learns complex temporal multi-modal sensorimotor relations in connection with external parameters and goals; produces movement trajectories in joint or task space; and executes these trajectories through a high-level feedback control loop. Conditioned with an external goal that is encoded in the sensorimotor space of the robot, predicted sensorimotor trajectory that is expected to be observed during the successful execution of the task is generated by the CNMP, and the corresponding motor commands are executed. In order to detect and react to unexpected events during action execution, CNMP is further conditioned with the actual sensor readings in each time-step. Through simulations and real robot experiments, we showed that CNMPs can learn the nonlinear relations between low-dimensional parameter spaces and complex movement trajectories from few demonstrations; and they can also model the associations between high-dimensional sensorimotor spaces and complex motions using large number of demonstrations. The experiments further showed that even the task parameters were not explicitly provided to the system, the robot could learn their influence by associating the learned sensorimotor representations with the movement trajectories. The robot, for example, learned the influence of object weights and shapes through exploiting its sensorimotor space that includes proprioception and force measurements; and be able to change the movement trajectory on the fly when one of these factors were changed through external intervention.