ELMP: 解析的方針勾配による効率的な動作計画学習
ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients
ニューラル動作プランナーを新しい環境に適応させる際、高価な専門家データの再収集を不要にし、微分可能な運動学レイヤーと自己教師あり微調整で直接ポリシーを最適化するフレームワークを提案。
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著者: Yixiao Li, Tifanny Portela, Jordis Herrmann, René Zurbrügg, Marco Hutter
分類: cs.RO
原文アブストラクト
Neural Motion Planners (NMPs) enable fast reactive motion generation, but adapting them to new environments typically requires recollecting large expert datasets, which is computationally prohibitive. We propose ELMP, a framework for data-efficient adaptation via self-supervised fine-tuning. Rather than generating additional expert trajectories with expensive global planners, ELMP directly optimizes the policy through a differentiable kinematic layer using dense collision, target-reaching, and smoothness objectives. This replaces expert data generation with rapid problem sampling, reducing per-sample adaptation cost by roughly two orders of magnitude. To further support robust generalization across changing kinematic chains, we introduce a mechanism to explicitly encode tool geometry via point clouds. Benchmarked against classical and neural baselines, ELMP achieves an 84.8% average success rate with orders-of-magnitude lower cold-start latency than classical methods. In unseen environments, self-supervised fine-tuning improves success rate from 57.3% (zero-shot) to 89.8%, removing the data collection bottleneck. Our approach maintains millisecond-level inference latency and is validated on a physical Franka Emika Panda robot.
