RAP: リスクを考慮した予測によるロバスト計画
RAP: Risk-Aware Prediction for Robust Planning
予測自体をリスク考慮型にし、リスク偏りのある軌道分布を学習することで、オンライン計画時のリスク評価を効率化し安全なリアルタイム動作を実現する手法を提案。
著者: Haruki Nishimura, Jean Mercat, Blake Wulfe, Rowan McAllister, Adrien Gaidon
分類: cs.LG, cs.RO
原文アブストラクト
Robust planning in interactive scenarios requires predicting the uncertain future to make risk-aware decisions. Unfortunately, due to long-tail safety-critical events, the risk is often under-estimated by finite-sampling approximations of probabilistic motion forecasts. This can lead to overconfident and unsafe robot behavior, even with robust planners. Instead of assuming full prediction coverage that robust planners require, we propose to make prediction itself risk-aware. We introduce a new prediction objective to learn a risk-biased distribution over trajectories, so that risk evaluation simplifies to an expected cost estimation under this biased distribution. This reduces the sample complexity of the risk estimation during online planning, which is needed for safe real-time performance. Evaluation results in a didactic simulation environment and on a real-world dataset demonstrate the effectiveness of our approach. The code and a demo are available.
