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PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning | | Aleksandra Faust
; Oscar Ramirez
; Marek Fiser
; Kenneth Oslund
; Anthony Francis
; James Davidson
; Lydia Tapia
; | | Date: |
11 Oct 2017 | | Abstract: | We present PRM-RL, a hierarchical method for long-range navigation task
completion that combines sampling-based path planning with reinforcement
learning (RL) agents. The RL agents learn short-range, point-to-point
navigation policies that capture robot dynamics and task constraints without
knowledge of the large-scale topology, while the sampling-based planners
provide an approximate map of the space of possible configurations of the robot
from which collision-free trajectories feasible for the RL agents can be
identified. The same RL agents are used to control the robot under the
direction of the planning, enabling long-range navigation. We use the
Probabilistic Roadmaps (PRMs) for the sampling-based planner. The RL agents are
constructed using feature-based and deep neural net policies in continuous
state and action spaces. We evaluate PRM-RL on two navigation tasks with
non-trivial robot dynamics: end-to-end differential drive indoor navigation in
office environments, and aerial cargo delivery in urban environments with load
displacement constraints. These evaluations included both simulated
environments and on-robot tests. Our results show improvement in navigation
task completion over both RL agents on their own and traditional sampling-based
planners. In the indoor navigation task, PRM-RL successfully completes up to
215 meters long trajectories under noisy sensor conditions, and the aerial
cargo delivery completes flights over 1000 meters without violating the task
constraints in an environment 63 million times larger than used in training. | | Source: | arXiv, 1710.3937 | | Services: | Forum | Review | PDF | Favorites | |
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