Learning at the ends: From hand to tool affordances in humanoid robots
Learning at the ends: From hand to tool affordances in humanoid robots
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最后学习：人形机器人从手到工具的可供性
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
J. Santos
中科院分区：
文献类型：
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作者：
Giovanni Saponaro;Pedro Vicente;Atabak Dehban;L. Jamone;Alexandre Bernardino;J. Santos
设计在不可预测的人类环境中成功运行的机器人面临的公开挑战之一是如何使它们能够预测它们可以对物体执行什么动作，以及它们的效果是什么，即感知物体可供性的能力。由于对所有可能的世界交互进行建模是不可行的，因此需要从经验中学习，这就提出了收集大量经验（即训练数据）的挑战。通常，操纵机器人使用自己的手（或类似的末端执行器）对外部物体进行操作，但在某些情况下可能需要使用工具；然而，可以合理地假设，虽然机器人可以用自己的双手收集许多感觉运动经验，但对于所有可能的人造工具来说，这不可能发生。因此，在本文中，我们研究了从手到工具可供性的发展转变：机器人通过徒手获得的哪些感觉运动技能可以用于工具的使用？通过采用视觉和运动想象机制来紧凑地表示不同的手部姿势，我们提出了一种概率模型来学习手部可供性，并且我们展示了该模型如何推广以估计以前未见过的工具的可供性，最终支持人形机器人中的规划、决策和工具选择任务。我们展示了 iCub 人形机器人的实验结果，并以手势可供性数据集的形式公开发布收集到的感觉运动数据。
One of the open challenges in designing robots that operate successfully in the unpredictable human environment is how to make them able to predict what actions they can perform on objects, and what their effects will be, i.e., the ability to perceive object affordances. Since modeling all the possible world interactions is unfeasible, learning from experience is required, posing the challenge of collecting a large amount of experiences (i.e., training data). Typically, a manipulative robot operates on external objects by using its own hands (or similar end-effectors), but in some cases the use of tools may be desirable; nevertheless, it is reasonable to assume that while a robot can collect many sensorimotor experiences using its own hands, this cannot happen for all possible human-made tools. Therefore, in this paper we investigate the developmental transition from hand to tool affordances: what sensorimotor skills that a robot has acquired with its bare hands can be employed for tool use? By employing a visual and motor imagination mechanism to represent different hand postures compactly, we propose a probabilistic model to learn hand affordances, and we show how this model can generalize to estimate the affordances of previously unseen tools, ultimately supporting planning, decision-making and tool selection tasks in humanoid robots. We present experimental results with the iCub humanoid robot, and we publicly release the collected sensorimotor data in the form of a hand posture affordances dataset.