Use of speech and motion cues for bootstrapping complex action learning in iCub

Emre Ugur, Jimmy Baraglia, Lars Schillingmann, Y. Nagai
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Abstract

Parental scaffolding is an important mechanism that speeds up infant sensorimotor development. Infants pay stronger attention to the features of the objects highlighted by parents, and their skills develop earlier than they would in isolation due to caregivers support. Parents are known to make modifications in infant-directed actions, called “motionese”, which is characterized by a wider range of motion, repetitive actions, and longer and more pauses between movements. Inspired from motionese, we previously realized a robotic system [1] where the affordances and effect prediction capabilities that are learned in the previous stages of development are used to bootstrap complex imitation and action learning with the help a cooperative tutor through motionese. With this system, a robot could learn new skills via imitation learning by extracting the important steps from the observed movement trajectory, and then encoding them as subgoals that it can fulfill. Considering the affordances provided by the objects in the environment, it found and sequentially executed the actions that are predicted to generate the desired effects and achieve the subgoals; achieving the overall goal of complete imitation. We showed that motionese can be used to bridge the gap between the interacting agents with different movement capabilities, such as the human tutors and the arm-hand robot we employed. Furthermore, our experimental data indicated that naïve tutors who are not informed about the imitation mechanisms of the robot, changed their teaching strategy, and started to display motionese.
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在iCub中使用语音和动作提示引导复杂动作学习
父母支架是促进婴儿感觉运动发育的重要机制。婴儿会更注意父母强调的物体的特征,并且由于照顾者的支持,他们的技能比孤立的情况下发展得更早。众所周知,父母会对婴儿指示的动作进行修改,称为“动作式”,其特点是动作范围更大,动作重复,动作之间停顿时间更长。受motionese的启发,我们之前实现了一个机器人系统[1],其中在先前开发阶段学习到的启示和效果预测能力被用于在motionese合作导师的帮助下引导复杂的模仿和动作学习。在这个系统中,机器人可以通过模仿学习的方式,从观察到的运动轨迹中提取重要的步骤,然后将它们编码为它可以完成的子目标,从而学习新的技能。考虑到环境中物体提供的启示,它发现并依次执行预期的动作,以产生预期的效果并实现子目标;达到完全模仿的总体目标。我们表明,motionese可以用来弥合具有不同运动能力的交互代理之间的差距,例如人类导师和我们使用的手臂机器人。此外,我们的实验数据表明,naïve导师在不了解机器人模仿机制的情况下,改变了他们的教学策略,并开始表现出motionese。
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