Robust Sensorimotor Representation to Physical Interaction Changes in Humanoid Motion Learning
Robust Sensorimotor Representation to Physical Interaction Changes in Humanoid Motion Learning
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DOI：
10.1109/tnnls.2014.2333092
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发表时间：
2015-05
影响因子：
10.4
通讯作者：
T. Shimizu;R. Saegusa;Shuhei Ikemoto;H. Ishiguro;G. Metta
中科院分区：
文献类型：
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作者：
T. Shimizu;R. Saegusa;Shuhei Ikemoto;H. Ishiguro;G. Metta
本文提出了一种基于运动特征相转移序列的演示学习系统。该系统旨在综合在教师支持交互过程中学习到的类人全身运动知识，并将这些知识应用于机器人与周围环境之间的不同物理交互中。相转移序列表示多个时间序列中变化点的时间顺序。它对序列的动态方面进行编码，以便吸收由于相互作用变化而产生的时序和幅度的间隙。通过一个真实的仿人机器人和兼容的模拟器，对其在仰卧起坐和行走运动强化学习中的相转移序列进行了评价。在这两项任务中，与传统的相似性测量相比，通过所提出的特征学习机器人运动对物理相互作用的依赖程度更低。相转移序列也提高了运动学习的收敛速度。我们提出的特征是原创性的，主要是因为它吸收了最初获得的物理相互作用变化带来的间隙，从而提高了后续相互作用中的学习速度。
This paper proposes a learning from demonstration system based on a motion feature, called phase transfer sequence. The system aims to synthesize the knowledge on humanoid whole body motions learned during teacher-supported interactions, and apply this knowledge during different physical interactions between a robot and its surroundings. The phase transfer sequence represents the temporal order of the changing points in multiple time sequences. It encodes the dynamical aspects of the sequences so as to absorb the gaps in timing and amplitude derived from interaction changes. The phase transfer sequence was evaluated in reinforcement learning of sitting-up and walking motions conducted by a real humanoid robot and compatible simulator. In both tasks, the robotic motions were less dependent on physical interactions when learned by the proposed feature than by conventional similarity measurements. Phase transfer sequence also enhanced the convergence speed of motion learning. Our proposed feature is original primarily because it absorbs the gaps caused by changes of the originally acquired physical interactions, thereby enhancing the learning speed in subsequent interactions.