Reaching control of a full-torso, modelled musculoskeletal robot using muscle synergies emergent under reinforcement learning
Reaching control of a full-torso, modelled musculoskeletal robot using muscle synergies emergent under reinforcement learning
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DOI：
10.1088/1748-3182/9/1/016015
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发表时间：
2014-03-01
影响因子：
3.4
通讯作者：
Holland, O. E.
中科院分区：
文献类型：
--
作者：
Diamond, A.;Holland, O. E.
“拟人”机器人模仿人类的形态和内部结构-骨骼，肌肉，顺应性和高冗余度-从而提出了一个艰巨的挑战，传统的控制。在这里，我们得到了一个新的控制器，这类机器人学习有效的达到行动，通过持续激活加权肌肉协同作用，一种方法，它利用令人信服的，最近的证据，从动物和人类的研究，但几乎是未开发的肌肉骨骼机器人文献。由于一个给定的机器人的有效协同模式将是未知的，我们推导出一个协同学习的方法，旨在让他们的出现，特别是那些模式，帮助线性化控制。使用一个广泛的基于物理的模型的拟人ECCERobot，我们发现，有效的达到行动可以学习，包括只有两个顺序的电机共激活模式，每个控制只有一个共同的驱动信号。因子分析表明，新兴的肌肉共激活，可以在很大程度上重建使用加权组合只有13个共同的片段。测试这些“候选”协同作为可驾驶的单位，同一个控制器现在学习达到任务更快，更好。
'Anthropomimetic' robots mimic both human morphology and internal structure-skeleton, muscles, compliance and high redundancy-thus presenting a formidable challenge to conventional control. Here we derive a novel controller for this class of robot which learns effective reaching actions through the sustained activation of weighted muscle synergies, an approach which draws upon compelling, recent evidence from animal and human studies, but is almost unexplored to date in the musculoskeletal robot literature. Since the effective synergy patterns for a given robot will be unknown, we derive a reinforcement-learning approach intended to allow their emergence, in particular those patterns aiding linearization of control. Using an extensive physics-based model of the anthropomimetic ECCERobot, we find that effective reaching actions can be learned comprising only two sequential motor co-activation patterns, each controlled by just a single common driving signal. Factor analysis shows the emergent muscle co-activations can be largely reconstructed using weighted combinations of only 13 common fragments. Testing these 'candidate' synergies as drivable units, the same controller now learns the reaching task both faster and better.