意味的観測を用いた実演からのナビゲーションコスト学習
Learning Navigation Costs from Demonstration with Semantic Observations
自動運転ロボットの実演から、意味的観測（車・歩道・車線など）に基づくコスト関数を逆強化学習で推定する手法を提案し、CARLAシミュレータで交通ルールに従う走行を学習できることを示した。
著者: Tianyu Wang, Vikas Dhiman, Nikolay Atanasov
分類: cs.LG, cs.RO, stat.ML
原文アブストラクト
This paper focuses on inverse reinforcement learning (IRL) for autonomous robot navigation using semantic observations. The objective is to infer a cost function that explains demonstrated behavior while relying only on the expert's observations and state-control trajectory. We develop a map encoder, which infers semantic class probabilities from the observation sequence, and a cost encoder, defined as deep neural network over the semantic features. Since the expert cost is not directly observable, the representation parameters can only be optimized by differentiating the error between demonstrated controls and a control policy computed from the cost estimate. The error is optimized using a closed-form subgradient computed only over a subset of promising states via a motion planning algorithm. We show that our approach learns to follow traffic rules in the autonomous driving CARLA simulator by relying on semantic observations of cars, sidewalks and road lanes.
