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.

翻译：互动情景的强力规划要求预测不确定的未来以做出风险意识决定。 不幸的是,由于长尾安全临界事件,风险往往被概率运动预测的有限抽样近似值低估。这可能导致过度自信和不安全机器人行为,即使有强健的规划者也是如此。 我们提议不假定强健的规划者需要充分的预测范围,而是提出预测本身的风险意识。 我们引入一个新的预测目标,以了解轨道上的风险偏差分布,从而使得风险评估简化到这种偏差分布下的预期成本估计。这降低了在线规划期间风险估计的抽样复杂性,而这是安全实时业绩所需要的。 在实验模拟环境中和现实世界数据集的评价结果证明了我们的方法的有效性。