D-SafeMPC: 拡散駆動型安全モデル予測制御と離散時間制御バリア関数
D-SafeMPC: Diffusion-Driven Safe Model Predictive Control with Discrete-Time Control Barrier Functions
拡散モデルによる軌道生成に制御バリア関数と制御リアプノフ関数を組み込み、各ノイズ除去ステップでMPCが軌道を修正することで、安全で実行可能な軌道を生成する手法を提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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4. どうやって有効だと検証した？
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著者: Erdi Sayar, Ersin Daş, Joel W. Burdick, Alois Knoll, Erdal Kayacan
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
A key limitation on the use of diffusion models in robotic planning is their inability to inherently enforce safety or dynamical constraints, which often results in physically infeasible or unsafe outputs. Hybrid approaches that employ model predictive control (MPC) to address this problem can be unstable, as poor trajectory initializations from the diffusion model prevent the MPC from converging to a safe and feasible solution. To overcome these challenges, we propose D-SafeMPC, which enhances the interaction between diffusion and control. Our method guides the reverse diffusion process with control barrier functions (CBFs) and control Lyapunov functions (CLFs) and employs an iterative-projection scheme where an MPC refines the trajectory at each denoising step. This steers sampling toward safe, goal-directed regions and provides reliable MPC warm starts. In simulations on a Franka manipulator across four scenarios (one static-obstacle and three dynamic-obstacle settings) and in a sim-to-real experiment on a physical Franka robot, D-SafeMPC improves safety, task success rates, and planning efficiency over state-of-the-art baselines. To facilitate reproducibility, our source code and experimental configurations are available in a repository at https://github.com/erdiphd/D-SafeMPC
