APPLR: Adaptive Planner Parameter Learning from Reinforcement

11/01/2020
by   Zifan Xu, et al.
0

Classical navigation systems typically operate using a fixed set of hand-picked parameters (e.g. maximum speed, sampling rate, inflation radius, etc.) and require heavy expert re-tuning in order to work in new environments. To mitigate this requirement, it has been proposed to learn parameters for different contexts in a new environment using human demonstrations collected via teleoperation. However, learning from human demonstration limits deployment to the training environment, and limits overall performance to that of a potentially-suboptimal demonstrator. In this paper, we introduce APPLR, Adaptive Planner Parameter Learning from Reinforcement, which allows existing navigation systems to adapt to new scenarios by using a parameter selection scheme discovered via reinforcement learning (RL) in a wide variety of simulation environments. We evaluate APPLR on a robot in both simulated and physical experiments, and show that it can outperform both a fixed set of hand-tuned parameters and also a dynamic parameter tuning scheme learned from human demonstration.

READ FULL TEXT

page 4

page 5

page 6

03/31/2020

APPLD: Adaptive Planner Parameter Learning from Demonstration

Existing autonomous robot navigation systems allow robots to move from o...
05/17/2021

APPL: Adaptive Planner Parameter Learning

While current autonomous navigation systems allow robots to successfully...
11/01/2020

APPLI: Adaptive Planner Parameter Learning From Interventions

While classical autonomous navigation systems can typically move robots ...
04/05/2021

UDO: Universal Database Optimization using Reinforcement Learning

UDO is a versatile tool for offline tuning of database systems for speci...
08/22/2021

APPLE: Adaptive Planner Parameter Learning from Evaluative Feedback

Classical autonomous navigation systems can control robots in a collisio...
03/08/2021

Iterative Program Synthesis for Adaptable Social Navigation

Robot social navigation is influenced by human preferences and environme...
10/16/2020

Agile Robot Navigation through Hallucinated Learning and Sober Deployment

Learning from Hallucination (LfH) is a recent machine learning paradigm ...