4,454 research outputs found
Accelerating Online Reinforcement Learning via Supervisory Safety Systems
Deep reinforcement learning (DRL) is a promising method to learn control
policies for robots only from demonstration and experience. To cover the whole
dynamic behaviour of the robot, the DRL training is an active exploration
process typically derived in simulation environments. Although this simulation
training is cheap and fast, applying DRL algorithms to real-world settings is
difficult. If agents are trained until they perform safely in simulation,
transferring them to physical systems is difficult due to the sim-to-real gap
caused by the difference between the simulation dynamics and the physical
robot.
In this paper, we present a method of online training a DRL agent to drive
autonomously on a physical vehicle by using a model-based safety supervisor.
Our solution uses a supervisory system to check if the action selected by the
agent is safe or unsafe and ensure that a safe action is always implemented on
the vehicle. With this, we can bypass the sim-to-real problem while training
the DRL algorithm safely, quickly, and efficiently. We provide a variety of
real-world experiments where we train online a small-scale, physical vehicle to
drive autonomously with no prior simulation training. The evaluation results
show that our method trains agents with improved sample efficiency while never
crashing, and the trained agents demonstrate better driving performance than
those trained in simulation.Comment: 7 Pages, 10 Figures, 1 Table. Submitted to 2023 IEEE International
Conference on Robotics and Automation (ICRA 2023
Beyond Basins of Attraction: Quantifying Robustness of Natural Dynamics
Properly designing a system to exhibit favorable natural dynamics can greatly
simplify designing or learning the control policy. However, it is still unclear
what constitutes favorable natural dynamics and how to quantify its effect.
Most studies of simple walking and running models have focused on the basins of
attraction of passive limit-cycles and the notion of self-stability. We instead
emphasize the importance of stepping beyond basins of attraction. We show an
approach based on viability theory to quantify robust sets in state-action
space. These sets are valid for the family of all robust control policies,
which allows us to quantify the robustness inherent to the natural dynamics
before designing the control policy or specifying a control objective. We
illustrate our formulation using spring-mass models, simple low dimensional
models of running systems. We then show an example application by optimizing
robustness of a simulated planar monoped, using a gradient-free optimization
scheme. Both case studies result in a nonlinear effective stiffness providing
more robustness.Comment: 15 pages. This work has been accepted to IEEE Transactions on
Robotics (2019
Winning the 3rd Japan Automotive AI Challenge -- Autonomous Racing with the Autoware.Auto Open Source Software Stack
The 3rd Japan Automotive AI Challenge was an international online autonomous
racing challenge where 164 teams competed in December 2021. This paper outlines
the winning strategy to this competition, and the advantages and challenges of
using the Autoware.Auto open source autonomous driving platform for multi-agent
racing. Our winning approach includes a lane-switching opponent overtaking
strategy, a global raceline optimization, and the integration of various tools
from Autoware.Auto including a Model-Predictive Controller. We describe the use
of perception, planning and control modules for high-speed racing applications
and provide experience-based insights on working with Autoware.Auto. While our
approach is a rule-based strategy that is suitable for non-interactive
opponents, it provides a good reference and benchmark for learning-enabled
approaches.Comment: Accepted at Autoware Workshop at IV 202
Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds
This paper presents a multi-layer motion planning and control architecture
for autonomous racing, capable of avoiding static obstacles, performing active
overtakes, and reaching velocities above 75 . The used offline global
trajectory generation and the online model predictive controller are highly
based on optimization and dynamic models of the vehicle, where the tires and
camber effects are represented in an extended version of the basic Pacejka
Magic Formula. The proposed single-track model is identified and validated
using multi-body motorsport libraries which allow simulating the vehicle
dynamics properly, especially useful when real experimental data are missing.
The fundamental regularization terms and constraints of the controller are
tuned to reduce the rate of change of the inputs while assuring an acceptable
velocity and path tracking. The motion planning strategy consists of a
Fren\'et-Frame-based planner which considers a forecast of the opponent
produced by a Kalman filter. The planner chooses the collision-free path and
velocity profile to be tracked on a 3 seconds horizon to realize different
goals such as following and overtaking. The proposed solution has been applied
on a Dallara AV-21 racecar and tested at oval race tracks achieving lateral
accelerations up to 25 .Comment: Accepted to the 25th IEEE International Conference on Intelligent
Transportation Systems (IEEE ITSC 2022
Assessing the Robustness of LiDAR, Radar and Depth Cameras Against Ill-Reflecting Surfaces in Autonomous Vehicles: An Experimental Study
Range-measuring sensors play a critical role in autonomous driving systems.
While LiDAR technology has been dominant, its vulnerability to adverse weather
conditions is well-documented. This paper focuses on secondary adverse
conditions and the implications of ill-reflective surfaces on range measurement
sensors. We assess the influence of this condition on the three primary ranging
modalities used in autonomous mobile robotics: LiDAR, RADAR, and Depth-Camera.
Based on accurate experimental evaluation the papers findings reveal that under
ill-reflectivity, LiDAR ranging performance drops significantly to 33% of its
nominal operating conditions, whereas RADAR and Depth-Cameras maintain up to
100% of their nominal distance ranging capabilities. Additionally, we
demonstrate on a 1:10 scaled autonomous racecar how ill-reflectivity adversely
impacts downstream robotics tasks, highlighting the necessity for robust range
sensing in autonomous driving.Comment: Accepted at IEEE 9th World Forum on Internet of Thing
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