In this study, we propose the use of the phase-amplitude reduction method to
construct an imitation learning framework. Imitating human movement
trajectories is recognized as a promising strategy for generating a range of
human-like robot movements. Unlike previous dynamical system-based imitation
learning approaches, our proposed method allows the robot not only to imitate a
limit cycle trajectory but also to replicate the transient movement from the
initial or disturbed state to the limit cycle. Consequently, our method offers
a safer imitation learning approach that avoids generating unpredictable
motions immediately after disturbances or from a specified initial state. We
first validated our proposed method by reconstructing a simple limit-cycle
attractor. We then compared the proposed approach with a conventional method on
a lemniscate trajectory tracking task with a simulated robot arm. Our findings
confirm that our proposed method can more accurately generate transient
movements to converge on a target periodic attractor compared to the previous
standard approach. Subsequently, we applied our method to a real robot arm to
imitate periodic human movements.

提出了一种利用相位幅度减小方法构建模仿学习框架的研究，用于模仿周期性人体运动。

基于相位幅度缩减的模仿学习

Phase-Amplitude Reduction-Based Imitation Learning

We develop a human movement trajectory prediction system that incorporates
the scene information (Scene-LSTM) as well as human movement trajectories
(Pedestrian movement LSTM) in the prediction process within static crowded
scenes. We superimpose a two-level grid structure (scene is divided into grid
cells each modeled by a scene-LSTM, which are further divided into smaller
sub-grids for finer spatial granularity) and explore common human trajectories
occurring in the grid cell (e.g., making a right or left turn onto sidewalks
coming out of an alley; or standing still at bus/train stops). Two coupled LSTM
networks, Pedestrian movement LSTMs (one per target) and the corresponding
Scene-LSTMs (one per grid-cell) are trained simultaneously to predict the next
movements. We show that such common path information greatly influences
prediction of future movement. We further design a scene data filter that holds
important non-linear movement information. The scene data filter allows us to
select the relevant parts of the information from the grid cell's memory
relative to a target's state. We evaluate and compare two versions of our
method with the Linear and several existing LSTM-based methods on five crowded
video sequences from the UCY [1] and ETH [2] datasets. The results show that
our method reduces the location displacement errors compared to related methods
and specifically about 80% reduction compared to social interaction methods.

本研究采用场景信息 (Scene-LSTM) 和人类运动轨迹 (Pedestrian movement LSTM) 结合的方式在静态人群中预测人类运动轨迹，其中引入了两层网格结构，探索出现在该网格单元中的常见人类轨迹，在训练预测模型中考虑了共性。相比线性模型和基于已有 LSTM 的方法，本研究所提出方法的目标位置误差有显著降低，与社交交互方法比较则降低了约 80% 的误差。