Synergy-based policy improvement with path integrals for anthropomorphic hands
Synergy-based policy improvement with path integrals for anthropomorphic hands
复制标题
基于协同的政策改进与拟人手的路径积分
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
B. Siciliano
中科院分区：
文献类型：
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作者：
F. Ficuciello;D. Zaccara;B. Siciliano
在这项工作中，一个基于协同的强化学习算法已被开发，赋予自主抓取能力拟人化的手。在存在高自由度的情况下，经典机器学习技术需要随着问题的大小而增加的迭代次数，因此不能确保解决方案的收敛。姿态协同作用的使用决定了搜索空间的降维，并允许最近的学习技术，如路径积分的政策改进，变得容易适用。其中一个关键点是采用一个合适的奖励函数代表任务的目标，并确保一步的性能评估。力闭合质量的把握在协同子空间已被选为性能评价的成本函数。在SCHUNK 5-Finger Hand上进行的实验证明了该算法的有效性，该算法在学习新的抓握和执行从功率到非常小的物体的高精度抓握的各种能力方面显示出与人类能力相当的技能。
In this work, a synergy-based reinforcement learning algorithm has been developed to confer autonomous grasping capabilities to anthropomorphic hands. In the presence of high degrees of freedom, classical machine learning techniques require a number of iterations that increases with the size of the problem, thus convergence of the solution is not ensured. The use of postural synergies determines dimensionality reduction of the search space and allows recent learning techniques, such as Policy Improvement with Path Integrals, to become easily applicable. A key point is the adoption of a suitable reward function representing the goal of the task and ensuring one-step performance evaluation. Force-closure quality of the grasp in the synergies subspace has been chosen as a cost function for performance evaluation. The experiments conducted on the SCHUNK 5-Finger Hand demonstrate the effectiveness of the algorithm showing skills comparable to human capabilities in learning new grasps and in performing a wide variety from power to high precision grasps of very small objects.