歩幅調整を伴う様式的人型ロボット歩行のためのオンラインDNN駆動非線形MPC
Online DNN-driven Nonlinear MPC for Stylistic Humanoid Robot Walking with Step Adjustment
人間の動作データで学習したDNNで歩行軌道を生成し、非線形MPCで着地位置をオンライン調整することで、人らしい歩容を保ちながら転倒防止や外乱耐性を実現した。
著者: Giulio Romualdi, Paolo Maria Viceconte, Lorenzo Moretti, Ines Sorrentino, Stefano Dafarra, Silvio Traversaro, Daniele Pucci
分類: cs.RO
原文アブストラクト
This paper presents a three-layered architecture that enables stylistic locomotion with online contact location adjustment. Our method combines an autoregressive Deep Neural Network (DNN) acting as a trajectory generation layer with a model-based trajectory adjustment and trajectory control layers. The DNN produces centroidal and postural references serving as an initial guess and regularizer for the other layers. Being the DNN trained on human motion capture data, the resulting robot motion exhibits locomotion patterns, resembling a human walking style. The trajectory adjustment layer utilizes non-linear optimization to ensure dynamically feasible center of mass (CoM) motion while addressing step adjustments. We compare two implementations of the trajectory adjustment layer: one as a receding horizon planner (RHP) and the other as a model predictive controller (MPC). To enhance MPC performance, we introduce a Kalman filter to reduce measurement noise. The filter parameters are automatically tuned with a Genetic Algorithm. Experimental results on the ergoCub humanoid robot demonstrate the system's ability to prevent falls, replicate human walking styles, and withstand disturbances up to 68 Newton. Website: https://sites.google.com/view/dnn-mpc-walking Youtube video: https://www.youtube.com/watch?v=x3tzEfxO-xQ
