APG-Planner：解析的方策勾配による視覚ベース高速軌道計画の学習
APG-Planner: Learning Vision-Based High-Speed Trajectory Planning via Analytic Policy Gradient
微分可能な軌道ロールアウトと解析的方策勾配を用いて視覚ベースの高速軌道計画器を学習し、実機で最大11m/sの飛行を実現した。
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著者: Jinwoo Lee, Geunsik Bae, Changseung Kim, Jiwoo Kim, Woojae Shin, Hyondong Oh
分類: cs.RO
原文アブストラクト
Vision-based high-speed navigation requires a trajectory planner that can reactively generate smooth, collision-free trajectories from onboard visual observations. Deep reinforcement learning offers a promising approach to vision-based trajectory planning, as it enables the optimization of sequential decision-making policies in complex environments. However, model-free policy gradient methods rely on sample-based policy gradient estimation, which yields noisy gradient estimates and inefficient training. To this end, we propose a framework for learning trajectory planning policies using the analytic policy gradient (APG) method and use it to train APG-Planner, a vision-based high-speed trajectory planner. The proposed framework leverages differential flatness to analytically reconstruct vehicle states from planned trajectories, thereby enabling differentiable trajectory-level state transitions without requiring an explicit low-level dynamics model. By iteratively performing trajectory planning and differentiable state transitions, we construct a differentiable episode rollout and analytically compute policy gradients from episode-level losses, improving both training efficiency and policy performance. After approximately 30 minutes of training on a single GPU, APG-Planner demonstrates comparable or superior performance to existing baseline methods in simulation and achieves speeds of up to 11\,m/s in real-world flight experiments. We further validate the proposed framework on a skid-steer ground robot, demonstrating its applicability to differentially flat robotic systems beyond quadrotors.