MPDM: Multi-policy Decision Making From Autonomous Driving to Social Robot Navigation
Abstract
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 28, 2018
- Accession Number
- AD1100223
Entities
People
- Alex G. Cunningham
- Dhanvin Mehta
- Edwin Olson
- Eric Galceran
- Gonzalo Ferrer
- Ryan M. Eustice
Organizations
- Toyota Research Institute