時空間チューブを用いた未知オイラー・ラグランジュ系の模倣学習
Learning from Demonstration via Spatiotemporal Tubes for Unknown Euler-Lagrange Systems
デモンストレーションをデータ駆動の安全仕様として捉え、時空間チューブを学習してフィードバック制御で追従する、未知のオイラー・ラグランジュ系向けの統合的模倣学習フレームワークを提案した。
著者: Ratnangshu Das, Puneeth Shankar, Varuni Buereddy, Ravi Prakash, Pushpak Jagtap
分類: cs.RO, eess.SY
原文アブストラクト
We present STT-LfD, a unified Learning from Demonstration (LfD) framework that integrates motion learning with control for unknown Euler-Lagrange systems. Unlike traditional decoupled approaches that track a fixed reference, the proposed method treats demonstrations as a data-driven safety specification. Using heteroscedastic Gaussian Processes, STT-LfD learns Spatiotemporal Tubes (STTs) as an intent envelope that capture time-varying precision requirements of a task. A closed-form feedback controller then enforces these learned constraints while respecting actuator limits, without requiring explicit system identification. The approach preserves the temporal structure of demonstrations, remains computationally efficient, and avoids explicit system identification. Hardware experiments on a mobile robot and a 7-DOF manipulator show that it outperforms baselines in robustness to disturbances and computational speed.