18 research outputs found
TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers
Model-predictive control (MPC) is a powerful tool for controlling highly
dynamic robotic systems subject to complex constraints. However, MPC is
computationally demanding, and is often impractical to implement on small,
resource-constrained robotic platforms. We present TinyMPC, a high-speed MPC
solver with a low memory footprint targeting the microcontrollers common on
small robots. Our approach is based on the alternating direction method of
multipliers (ADMM) and leverages the structure of the MPC problem for
efficiency. We demonstrate TinyMPC both by benchmarking against the
state-of-the-art solver OSQP, achieving nearly an order of magnitude speed
increase, as well as through hardware experiments on a 27 g quadrotor,
demonstrating high-speed trajectory tracking and dynamic obstacle avoidance.Comment: First three authors contributed equally and are ordered
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OmniDrones: An Efficient and Flexible Platform for Reinforcement Learning in Drone Control
In this work, we introduce OmniDrones, an efficient and flexible platform
tailored for reinforcement learning in drone control, built on Nvidia's
Omniverse Isaac Sim. It employs a bottom-up design approach that allows users
to easily design and experiment with various application scenarios on top of
GPU-parallelized simulations. It also offers a range of benchmark tasks,
presenting challenges ranging from single-drone hovering to over-actuated
system tracking. In summary, we propose an open-sourced drone simulation
platform, equipped with an extensive suite of tools for drone learning. It
includes 4 drone models, 5 sensor modalities, 4 control modes, over 10
benchmark tasks, and a selection of widely used RL baselines. To showcase the
capabilities of OmniDrones and to support future research, we also provide
preliminary results on these benchmark tasks. We hope this platform will
encourage further studies on applying RL to practical drone systems.Comment: Submitted to IEEE RA-
HyperPPO: A scalable method for finding small policies for robotic control
Models with fewer parameters are necessary for the neural control of
memory-limited, performant robots. Finding these smaller neural network
architectures can be time-consuming. We propose HyperPPO, an on-policy
reinforcement learning algorithm that utilizes graph hypernetworks to estimate
the weights of multiple neural architectures simultaneously. Our method
estimates weights for networks that are much smaller than those in common-use
networks yet encode highly performant policies. We obtain multiple trained
policies at the same time while maintaining sample efficiency and provide the
user the choice of picking a network architecture that satisfies their
computational constraints. We show that our method scales well - more training
resources produce faster convergence to higher-performing architectures. We
demonstrate that the neural policies estimated by HyperPPO are capable of
decentralized control of a Crazyflie2.1 quadrotor. Website:
https://sites.google.com/usc.edu/hyperppoComment: Website: https://sites.google.com/usc.edu/hyperpp
Robust Reinforcement Learning Algorithm for Vision-based Ship Landing of UAVs
This paper addresses the problem of developing an algorithm for autonomous
ship landing of vertical take-off and landing (VTOL) capable unmanned aerial
vehicles (UAVs), using only a monocular camera in the UAV for tracking and
localization. Ship landing is a challenging task due to the small landing
space, six degrees of freedom ship deck motion, limited visual references for
localization, and adversarial environmental conditions such as wind gusts. We
first develop a computer vision algorithm which estimates the relative position
of the UAV with respect to a horizon reference bar on the landing platform
using the image stream from a monocular vision camera on the UAV. Our approach
is motivated by the actual ship landing procedure followed by the Navy
helicopter pilots in tracking the horizon reference bar as a visual cue. We
then develop a robust reinforcement learning (RL) algorithm for controlling the
UAV towards the landing platform even in the presence of adversarial
environmental conditions such as wind gusts. We demonstrate the superior
performance of our algorithm compared to a benchmark nonlinear PID control
approach, both in the simulation experiments using the Gazebo environment and
in the real-world setting using a Parrot ANAFI quad-rotor and sub-scale ship
platform undergoing 6 degrees of freedom (DOF) deck motion