Juett Jonathan, Kuipers Benjamin
Computer Science and Engineering, University of Michigan, Ann Arbor, MI, United States.
Front Neurorobot. 2019 Feb 22;13:4. doi: 10.3389/fnbot.2019.00004. eCollection 2019.
The young infant explores its body, its sensorimotor system, and the immediately accessible parts of its environment, over the course of a few months creating a model of peripersonal space useful for reaching and grasping objects around it. Drawing on constraints from the empirical literature on infant behavior, we present a preliminary computational model of this learning process, implemented and evaluated on a physical robot. The learning agent explores the relationship between the configuration space of the arm, sensing joint angles through proprioception, and its visual perceptions of the hand and grippers. The resulting knowledge is represented as the peripersonal space (PPS) graph, where nodes represent states of the arm, edges represent safe movements, and paths represent safe trajectories from one pose to another. In our model, the learning process is driven by a form of intrinsic motivation. When repeatedly performing an action, the agent learns the typical result, but also detects unusual outcomes, and is motivated to learn how to make those unusual results reliable. Arm motions typically leave the static background unchanged, but occasionally bump an object, changing its static position. The reach action is learned as a reliable way to bump and move a specified object in the environment. Similarly, once a reliable reach action is learned, it typically makes a quasi-static change in the environment, bumping an object from one static position to another. The unusual outcome is that the object is accidentally grasped (thanks to the innate Palmar reflex), and thereafter moves dynamically with the hand. Learning to make grasping reliable is more complex than for reaching, but we demonstrate significant progress. Our current results are steps toward autonomous sensorimotor learning of motion, reaching, and grasping in peripersonal space, based on unguided exploration and intrinsic motivation.
在几个月的时间里，婴儿探索自己的身体、感觉运动系统以及周围环境中伸手可及的部分，从而建立起一个个人周边空间模型，这有助于其够取和抓握周围的物体。借鉴关于婴儿行为的实证文献中的限制条件，我们提出了一个关于此学习过程的初步计算模型，并在一个物理机器人上进行了实现和评估。学习智能体探索手臂配置空间、通过本体感觉感知关节角度以及对手和抓取器的视觉感知之间的关系。所得到的知识被表示为个人周边空间（PPS）图，其中节点表示手臂的状态，边表示安全运动，路径表示从一个姿势到另一个姿势的安全轨迹。在我们的模型中，学习过程由一种内在动机驱动。当反复执行一个动作时，智能体学习到典型结果，但也会检测到不寻常的结果，并被激励去学习如何使那些不寻常的结果变得可靠。手臂运动通常不会改变静态背景，但偶尔会碰到一个物体，改变其静态位置。够取动作被学习为在环境中碰撞和移动指定物体的可靠方式。同样，一旦学习到一个可靠的够取动作，它通常会在环境中产生一个准静态变化，将一个物体从一个静态位置碰撞到另一个静态位置。不寻常的结果是物体被意外抓取（由于天生的抓握反射），此后随着手一起动态移动。学习使抓握变得可靠比学习够取更复杂，但我们展示了显著的进展。我们目前的结果是朝着基于无引导探索和内在动机在个人周边空间中自主进行运动、够取和抓握的感觉运动学习迈出的步伐。
