マルチステップ四 rotor 運動予測のための時間畳み込み
Temporal Convolutions for Multi-Step Quadrotor Motion Prediction
時間畳み込みネットワーク(TCN)をseq2seq的に用い、将来の制御入力を統合して四 rotor の多段階運動予測を1回の順伝播で行うEnd2End-TCNを提案し、屋内飛行データで誤差を55%削減した。
著者: Samuel Looper, Steven L. Waslander
分類: cs.RO, cs.LG
原文アブストラクト
Model-based control methods for robotic systems such as quadrotors, autonomous driving vehicles and flexible manipulators require motion models that generate accurate predictions of complex nonlinear system dynamics over long periods of time. Temporal Convolutional Networks (TCNs) can be adapted to this challenge by formulating multi-step prediction as a sequence-to-sequence modeling problem. We present End2End-TCN: a fully convolutional architecture that integrates future control inputs to compute multi-step motion predictions in one forward pass. We demonstrate the approach with a thorough analysis of TCN performance for the quadrotor modeling task, which includes an investigation of scaling effects and ablation studies. Ultimately, End2End-TCN provides 55% error reduction over the state of the art in multi-step prediction on an aggressive indoor quadrotor flight dataset. The model yields accurate predictions across 90 timestep horizons over a 900 ms interval.
