Dynamic motions of humans and robots are widely driven by posture-dependent nonlinear interactions between their degrees of freedom. However, these dynamical effects remain mostly overlooked when studying the mechanisms of human movement generation. Inspired by recent works, we hypothesize that human motions are planned as sequences of geodesic synergies, and thus correspond to coordinated joint movements achieved with piecewise minimum energy. The underlying computational model is built on Riemannian geometry to account for the inertial characteristics of the body. Through the analysis of various human arm motions, we find that our model segments motions into geodesic synergies, and successfully predicts observed arm postures, hand trajectories, as well as their respective velocity profiles. Moreover, we show that our analysis can further be exploited to transfer arm motions to robots by reproducing individual human synergies as geodesic paths in the robot configuration space.
翻译:人类和机器人的动态运动广泛受到其自由度之间基于姿态的非线性互动的广泛驱动。 但是,在研究人类运动生成机制时,这些动态效应仍然大都被忽视。 在近期的作品的启发下,我们假设人类运动是规划成大地测量协同作用的序列,因此与以小节能最小能量实现的协调联合运动相对应。基本计算模型建在里曼尼的几何学上,以考虑身体的惯性特征。通过分析各种人类手臂运动,我们发现我们的模型部分移动为大地测量协同作用,并成功预测了观察到的手臂姿态、手轨迹以及各自的速度特征。此外,我们还表明,我们的分析可以进一步被利用,通过在机器人配置空间中作为大地测量路径再生成个体的人类协同作用,将手臂运动转移到机器人身上。