1 research outputs found
Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning
Designing effective low-level robot controllers often entail
platform-specific implementations that require manual heuristic parameter
tuning, significant system knowledge, or long design times. With the rising
number of robotic and mechatronic systems deployed across areas ranging from
industrial automation to intelligent toys, the need for a general approach to
generating low-level controllers is increasing. To address the challenge of
rapidly generating low-level controllers, we argue for using model-based
reinforcement learning (MBRL) trained on relatively small amounts of
automatically generated (i.e., without system simulation) data. In this paper,
we explore the capabilities of MBRL on a Crazyflie centimeter-scale quadrotor
with rapid dynamics to predict and control at <50Hz. To our knowledge, this is
the first use of MBRL for controlled hover of a quadrotor using only on-board
sensors, direct motor input signals, and no initial dynamics knowledge. Our
controller leverages rapid simulation of a neural network forward dynamics
model on a GPU-enabled base station, which then transmits the best current
action to the quadrotor firmware via radio. In our experiments, the quadrotor
achieved hovering capability of up to 6 seconds with 3 minutes of experimental
training data.Comment: Accepted to IROS and RA-L, 2019. For more information, see the
website: https://sites.google.com/berkeley.edu/mbrl-quadrotor/. 9 pages, 12
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