论文标题
Riemannian对人类运动分析和重新定位的看法
A Riemannian Take on Human Motion Analysis and Retargeting
论文作者
论文摘要
人类和机器人的动态运动是由姿势依赖性的非线性相互作用在自由度之间的广泛驱动的。但是，在研究人类运动产生的机制时，这些动力学效应仍被忽视。受最近作品的启发，我们假设人类运动计划为地球协同序列，因此对应于用分段最小能量实现的协调关节运动。基础计算模型建立在Riemannian几何形状上，以解释身体的惯性特征。通过对各种人类手臂运动的分析，我们发现我们的模型段动作变成了测量协同作用，并成功预测了观察到的手臂姿势，手动轨迹及其各自的速度曲线。此外，我们表明我们的分析可以进一步利用，以通过将单个人类协同作用作为机器人配置空间中的地理路径转移到机器人中。
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.
