Real-time behaviour synthesis for dynamic hand-manipulation
Real-time behaviour synthesis for dynamic hand-manipulation
复制标题
动态手动操作的实时行为合成
DOI：
--
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
E. Todorov
中科院分区：
文献类型：
--
作者：
Vikash Kumar;Yuval Tassa;Tom Erez;E. Todorov
灵巧的手部操作是最复杂的生物运动类型之一，并且已被证明很难在机器人中复制。通常的机器人控制方法——遵循预先定义的轨迹或使用简化模型在线规划——都不适用。灵巧操作对接触力和物体位置的微小变化非常敏感，似乎需要在线规划而不需要任何简化。在这里，我们首次展示了在线规划（或模型预测控制）与人形手的完整物理模型，具有28个自由度和48个气动执行器。我们通过运动协同来增加驱动空间，在不去除灵巧性的情况下加速优化。我们的大多数结果都是模拟的，显示了不可掌握的对象操作以及打字。在这两种情况下，系统的输入都是高级任务描述，而手部运动的所有细节都是通过全自动数值优化在线出现的。
Dexterous hand manipulation is one of the most complex types of biological movement, and has proven very difficult to replicate in robots. The usual approaches to robotic control - following pre-defined trajectories or planning online with reduced models - are both inapplicable. Dexterous manipulation is so sensitive to small variations in contact force and object location that it seems to require online planning without any simplifications. Here we demonstrate for the first time online planning (or model-predictive control) with a full physics model of a humanoid hand, with 28 degrees of freedom and 48 pneumatic actuators. We augment the actuation space with motor synergies which speed up optimization without removing dexterity. Most of our results are in simulation, showing non-prehensile object manipulation as well as typing. In both cases the input to the system is a high level task description, while all details of the hand movement emerge online from fully automated numerical optimization.