MPDM: Multi-policy Decision Making From Autonomous Driving to Social Robot Navigation
Toyota Research Institute Ann Arbor United States
Pagination or Media Count:
This chapter presents Multi-Policy Decision-Making MPDM a novel approach to navigating in dynamic multi-agent environments. Rather than planning the trajectory of the robot explicitly, the planning process selects one of a set of closed-loop behaviors whose utility can be predicted through forward simulation that capture the complex interactions between the actions of these agents. These polices capture different high-level behavior and intentions, such as driving along a lane, turning at an intersection, or following pedestrians. We present two different scenarios where MPDM has been applied successfully An autonomous driving environment that models vehicle behavior for both our vehicle and nearby vehicles and a social environment, where multiple agents or pedestrians configure a dynamic environment for autonomous robot navigation. We present extensive validation for MPDM on both scenarios, using simulated and real-world experiments.
- Navigation and Guidance