  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.
