Author Archives: Yuanfu Luo

The overall goal of this project is to integrate planning and learning to enable a robot vehicle to drive autonomously among many pedestrians and vehicles.

PORCA

Y.F. Luo, P.P. Cai, A. Bera, D. Hsu, W.S. Lee, and D. Manocha. PORCA: Modeling and planning for autonomous driving among many pedestrians. In IEEE Robotics & Automation Letters. 2018.

This projects investigates a planning system for autonomous driving among many pedestrians. A key ingredient of our approach is a motion prediction model for pedestrians and vehicles. It accounts for both a pedestrian’s global navigation intention and local interactions with the vehicle and other pedestrians. Unfortunately, the autonomous vehicle does not know the pedestrians’ intentions a priori and requires a planning algorithm that hedges against the uncertainty in pedestrian intentions. Our planning system combines a POMDP algorithm with the pedestrian motion model and runs in real time. Experiments show that it enables a robot scooter to drive safely, efficiently, and smoothly in a crowd with a density of nearly one person per square meter.

 

Importance Sampling for Online Planning Under Uncertainty

Abstract

The partially observable Markov decision process (POMDP) provides a principled general framework for robot planning under uncertainty. Leveraging the idea of Monte Carlo sampling, recent POMDP planning algorithms have scaled up to various challenging robotic tasks, including, e.g., real-time online planning for autonomous vehicles. To further improve online planning performance, this paper presents IS-DESPOT, which introduces importance sampling to DESPOT, a state-of-the-art sampling-based POMDP algorithm for planning under uncertainty. Importance sampling improves DESPOT’s performance when there are critical, but rare events, which are difficult to sample. We prove that IS-DESPOT retains the theoretical guarantee of DESPOT. We demonstrate empirically that importance sampling significantly improves the performance of online POMDP planning for suitable tasks. We also present a general method for learning the importance sampling distribution.

Video

References

  • Y.F. Luo, H.Y. Bai, D. Hsu, and W.S. Lee. Importance sampling for online planning under uncertainty. In Algorithmic Foundations of Robotics XII – Proc. Int. Workshop on the Algorithmic Foundations of Robotics (WAFR). 2016.
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