RAP: Risk-Aware Prediction for Robust PlanningDownload PDF

Published: 10 Sept 2022, Last Modified: 17 Sept 2023CoRL 2022 OralReaders: Everyone
Student First Author: no
Keywords: Risk Measures, Forecasting, Safety, Human-Robot Interaction
TL;DR: a framework to perform risk-aware prediction, which facilitates robust planning
Abstract: 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 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.
Supplementary Material: zip
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/arxiv:2210.01368/code)
Website: https://sites.google.com/view/corl-risk/home
Code: https://github.com/TRI-ML/RAP
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