安全なクアッドロータ航法のための制約付き微分可能方策学習
Constrained Differentiable Policy Learning for Safe Quadrotor Navigation
微分可能シミュレータと主双対勾配法を用いて障害物回避を明示的な制約として定式化し、安全境界付近で有用な勾配を与える有界クリアランス制約とLME集約を導入することで、安全性能のトレードオフを改善するクアッドロータ方策を学習した。
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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著者: Yan Rui Tan, Praveen Krishnapur, Zeyu Feng, Han Bao, Yew-Soon Ong, Fan Shi
分類: cs.RO
原文アブストラクト
Differentiable simulation enables efficient policy optimization through analytic gradients, yet safety is commonly incorporated as a weighted penalty, requiring manual tuning of the trade-off between task performance and constraint satisfaction. We instead formulate obstacle avoidance as an explicit constraint and train quadrotor policies using primal--dual gradient descent--ascent through a differentiable simulator. We introduce two key components to enable effective constrained training. First, we propose a bounded clearance-based constraint function that provides informative gradients around the safety boundary while preventing large violations from destabilizing primal--dual optimization. Second, we introduce log-mean-exp (LME) aggregation to smoothly emphasize critical per-step safety violations across a trajectory and training batch within a single constraint governed by one dual variable. Together, these components enable the desired safety level to be specified directly while balancing constraint satisfaction and task completion. We theoretically prove that the proposed optimization with differentiable simulation converges to feasible policy under mild assumptions. Experiments demonstrate improved safety--performance trade-offs over fixed-penalty and existing policy-learning baselines, as well as generalization to denser out-of-distribution environments. Finally, the learned policy transfers zero-shot to Gazebo/PX4 and a physical quadrotor without additional fine-tuning.
