学習ベースのロバスト制御：自由エネルギーによる信頼性の高いロボティクスのための探索と分布的ロバスト性の統合
Learning-Based Robust Control: Unifying Exploration and Distributional Robustness for Reliable Robotics via Free Energy
自由エネルギー原理に基づき、環境ダイナミクスと報酬を同時に学習しつつ、認識的不確実性に対するロバスト性を保証するポリシー計算モデルを提案し、シミュレーションと実機で有効性を検証した。
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著者: Hozefa Jesawada, Giovanni Russo, Abdalla Swikir, Fares Abu-Dakka
分類: cs.RO, math.OC
原文アブストラクト
A key challenge towards reliable robotic control is devising computational models that can both learn policies and guarantee robustness when deployed in the field. Inspired by the free energy principle in computational neuroscience, to address these challenges, we propose a model for policy computation that jointly learns environment dynamics and rewards, while ensuring robustness to epistemic uncertainties. Expounding a distributionally robust free energy principle, we propose a modification to the maximum diffusion learning framework. After explicitly characterizing robustness of our policies to epistemic uncertainties in both environment and reward, we validate their effectiveness on continuous-control benchmarks, via both simulations and real-world experiments involving manipulation with a Franka Research~3 arm. Across simulation and zero-shot deployment, our approach narrows the sim-to-real gap, and enables repeatable tabletop manipulation without task-specific fine-tuning.
