1,535 research outputs found
ART/ATK: A research platform for assessing and mitigating the sim-to-real gap in robotics and autonomous vehicle engineering
We discuss a platform that has both software and hardware components, and
whose purpose is to support research into characterizing and mitigating the
sim-to-real gap in robotics and vehicle autonomy engineering. The software is
operating-system independent and has three main components: a simulation engine
called Chrono, which supports high-fidelity vehicle and sensor simulation; an
autonomy stack for algorithm design and testing; and a development environment
that supports visualization and hardware-in-the-loop experimentation. The
accompanying hardware platform is a 1/6th scale vehicle augmented with
reconfigurable mountings for computing, sensing, and tracking. Since this
vehicle platform has a digital twin within the simulation environment, one can
test the same autonomy perception, state estimation, or controls algorithms, as
well as the processors they run on, in both simulation and reality. A
demonstration is provided to show the utilization of this platform for autonomy
research. Future work will concentrate on augmenting ART/ATK with support for a
full-sized Chevy Bolt EUV, which will be made available to this group in the
immediate future.Comment: 4 pages, Presented at IROS 2022 Workshop on Miniature Robot Platforms
for Full Scale Autonomous Vehicle Research. arXiv admin note: substantial
text overlap with arXiv:2206.0653
MARBLER: An Open Platform for Standarized Evaluation of Multi-Robot Reinforcement Learning Algorithms
Multi-agent reinforcement learning (MARL) has enjoyed significant recent
progress, thanks to deep learning. This is naturally starting to benefit
multi-robot systems (MRS) in the form of multi-robot RL (MRRL). However,
existing infrastructure to train and evaluate policies predominantly focus on
challenges in coordinating virtual agents, and ignore characteristics important
to robotic systems. Few platforms support realistic robot dynamics, and fewer
still can evaluate Sim2Real performance of learned behavior. To address these
issues, we contribute MARBLER: Multi-Agent RL Benchmark and Learning
Environment for the Robotarium. MARBLER offers a robust and comprehensive
evaluation platform for MRRL by marrying Georgia Tech's Robotarium (which
enables rapid prototyping on physical MRS) and OpenAI's Gym framework (which
facilitates standardized use of modern learning algorithms). MARBLER offers a
highly controllable environment with realistic dynamics, including barrier
certificate-based obstacle avoidance. It allows anyone across the world to
train and deploy MRRL algorithms on a physical testbed with reproducibility.
Further, we introduce five novel scenarios inspired by common challenges in MRS
and provide support for new custom scenarios. Finally, we use MARBLER to
evaluate popular MARL algorithms and provide insights into their suitability
for MRRL. In summary, MARBLER can be a valuable tool to the MRS research
community by facilitating comprehensive and standardized evaluation of learning
algorithms on realistic simulations and physical hardware. Links to our
open-source framework and the videos of real-world experiments can be found at
https://shubhlohiya.github.io/MARBLER/.Comment: 7 pages, 3 figures, submitted to MRS 2023, for the associated
website, see https://shubhlohiya.github.io/MARBLER
Learning Real-world Autonomous Navigation by Self-Supervised Environment Synthesis
Machine learning approaches have recently enabled autonomous navigation for
mobile robots in a data-driven manner. Since most existing learning-based
navigation systems are trained with data generated in artificially created
training environments, during real-world deployment at scale, it is inevitable
that robots will encounter unseen scenarios, which are out of the training
distribution and therefore lead to poor real-world performance. On the other
hand, directly training in the real world is generally unsafe and inefficient.
To address this issue, we introduce Self-supervised Environment Synthesis
(SES), in which, after real-world deployment with safety and efficiency
requirements, autonomous mobile robots can utilize experience from the
real-world deployment, reconstruct navigation scenarios, and synthesize
representative training environments in simulation. Training in these
synthesized environments leads to improved future performance in the real
world. The effectiveness of SES at synthesizing representative simulation
environments and improving real-world navigation performance is evaluated via a
large-scale deployment in a high-fidelity, realistic simulator and a
small-scale deployment on a physical robot
Difference-based Deep Convolutional Neural Network for Simulation-to-reality UAV Fault Diagnosis
Identifying the fault in propellers is important to keep quadrotors operating
safely and efficiently. The simulation-to-reality (sim-to-real) UAV fault
diagnosis methods provide a cost-effective and safe approach to detect the
propeller faults. However, due to the gap between simulation and reality,
classifiers trained with simulated data usually underperform in real flights.
In this work, a new deep neural network (DNN) model is presented to address the
above issue. It uses the difference features extracted by deep convolutional
neural networks (DDCNN) to reduce the sim-to-real gap. Moreover, a new domain
adaptation method is presented to further bring the distribution of the
real-flight data closer to that of the simulation data. The experimental
results show that the proposed approach can achieve an accuracy of 97.9\% in
detecting propeller faults in real flight. Feature visualization was performed
to help better understand our DDCNN model.Comment: 7 pages, 8 figure
Sim2real and Digital Twins in Autonomous Driving: A Survey
Safety and cost are two important concerns for the development of autonomous
driving technologies. From the academic research to commercial applications of
autonomous driving vehicles, sufficient simulation and real world testing are
required. In general, a large scale of testing in simulation environment is
conducted and then the learned driving knowledge is transferred to the real
world, so how to adapt driving knowledge learned in simulation to reality
becomes a critical issue. However, the virtual simulation world differs from
the real world in many aspects such as lighting, textures, vehicle dynamics,
and agents' behaviors, etc., which makes it difficult to bridge the gap between
the virtual and real worlds. This gap is commonly referred to as the reality
gap (RG). In recent years, researchers have explored various approaches to
address the reality gap issue, which can be broadly classified into two
categories: transferring knowledge from simulation to reality (sim2real) and
learning in digital twins (DTs). In this paper, we consider the solutions
through the sim2real and DTs technologies, and review important applications
and innovations in the field of autonomous driving. Meanwhile, we show the
state-of-the-arts from the views of algorithms, models, and simulators, and
elaborate the development process from sim2real to DTs. The presentation also
illustrates the far-reaching effects of the development of sim2real and DTs in
autonomous driving
Autonomous shock sensing using bi-stable triboelectric generators and MEMS electrostatic levitation actuators
This work presents an automatic threshold shock-sensing trigger system that consists of a bi-stable triboelectric transducer and a levitation-based electrostatic mechanism. The bi-stable mechanism is sensitive to mechanical shocks and releases impact energy when the shock is strong enough. A triboelectric generator produces voltage when it receives a mechanical shock. The voltage is proportional to the mechanical shock. When the voltage exceed a certain level, the initially pulled-in Microelectromechanical system (MEMS) switch is opened and can disconnect the current in a safety electronic system. The MEMS switch combines two mechanisms of gap-closing (parallel-plate electrodes) with electrostatic levitation (side electrodes) to provide bi-directional motions. The switch is initially closed from a small bias voltage on the gap-closing electrodes. The voltage from the bi-stable generator is connected to the side electrodes. When the shock goes beyond a threshold, the upward force caused by the side electrodes on the switch becomes strong enough to peel off the switch from the closed position. The threshold shock the system can detect is tunable using two
control parameters. These two tuning parameters are the axial force on the bi- stable system (clamped-clamped beam) and the bias voltage on the MEMS switch (gap-closing electrodes). The actuation in macro-scale is thus directly connected to a sensor-switch mechanism in micro-scale. This chain makes an autonomous actuation and sensing stand-alone system that has potential application on air bag deployment devices and powerline protection systems. We provide a theoretical frame work of the entire system validated by experimental results
Contrastive Learning for Enhancing Robust Scene Transfer in Vision-based Agile Flight
Scene transfer for vision-based mobile robotics applications is a highly
relevant and challenging problem. The utility of a robot greatly depends on its
ability to perform a task in the real world, outside of a well-controlled lab
environment. Existing scene transfer end-to-end policy learning approaches
often suffer from poor sample efficiency or limited generalization
capabilities, making them unsuitable for mobile robotics applications. This
work proposes an adaptive multi-pair contrastive learning strategy for visual
representation learning that enables zero-shot scene transfer and real-world
deployment. Control policies relying on the embedding are able to operate in
unseen environments without the need for finetuning in the deployment
environment. We demonstrate the performance of our approach on the task of
agile, vision-based quadrotor flight. Extensive simulation and real-world
experiments demonstrate that our approach successfully generalizes beyond the
training domain and outperforms all baselines
Towards self-attention based visual navigation in the real world
Vision guided navigation requires processing complex visual information to
inform task-orientated decisions. Applications include autonomous robots,
self-driving cars, and assistive vision for humans. A key element is the
extraction and selection of relevant features in pixel space upon which to base
action choices, for which Machine Learning techniques are well suited. However,
Deep Reinforcement Learning agents trained in simulation often exhibit
unsatisfactory results when deployed in the real-world due to perceptual
differences known as the . An approach that is yet to be
explored to bridge this gap is self-attention. In this paper we (1) perform a
systematic exploration of the hyperparameter space for self-attention based
navigation of 3D environments and qualitatively appraise behaviour observed
from different hyperparameter sets, including their ability to generalise; (2)
present strategies to improve the agents' generalisation abilities and
navigation behaviour; and (3) show how models trained in simulation are capable
of processing real world images meaningfully in real time. To our knowledge,
this is the first demonstration of a self-attention based agent successfully
trained in navigating a 3D action space, using less than 4000 parameters.Comment: Submitted to The 2022 Australian Conference on Robotics and
Automation (ACRA 2022
Simulation and Visualisation Software for an Elastic Aircraft for High Altitudes based on Game Engine Technology
The aim of this thesis work was to design and develop a simulation and visualization platform based on game engine technology, that could be applied to any robotic system and would provide tools for representing the robot, visualizing the environment around it in a high level of detail and also provide means of sampling this environment in order to enable external simulation of interactions between the robot and its surroundings. The main design goal is for the platform to be able to have external physics simulations (robot and robot-environment interactions) entirely separated from the game engine environment. To this end, Unreal Engine 4 (UE4) has been chosen and the platform was implemented as a modular UE4 project, by making use of engine-specific structures. Interfacing between these modules and external ones has been achieved by designing and implementing a middleware interface for the platform, therefore enabling access to the middlewares data transfer system. Finally, this software-in-the-loop chain created between the UE4 modules and the external modules with the middleware as a transfer point has been evaluated in terms of feasibility and functionality by conducting tests on the various modules and interfaces thereof. The outcome is a powerful, flexible and ready-to-use simulation and visualization platform that can be easily adapted to any robotic system and provides the necessary means to accurately sample a customizable, high-quality environment in the vicinity of the robot
Dynamic Handover: Throw and Catch with Bimanual Hands
Humans throw and catch objects all the time. However, such a seemingly common
skill introduces a lot of challenges for robots to achieve: The robots need to
operate such dynamic actions at high-speed, collaborate precisely, and interact
with diverse objects. In this paper, we design a system with two multi-finger
hands attached to robot arms to solve this problem. We train our system using
Multi-Agent Reinforcement Learning in simulation and perform Sim2Real transfer
to deploy on the real robots. To overcome the Sim2Real gap, we provide multiple
novel algorithm designs including learning a trajectory prediction model for
the object. Such a model can help the robot catcher has a real-time estimation
of where the object will be heading, and then react accordingly. We conduct our
experiments with multiple objects in the real-world system, and show
significant improvements over multiple baselines. Our project page is available
at \url{https://binghao-huang.github.io/dynamic_handover/}.Comment: Accepted at CoRL 2023.
https://binghao-huang.github.io/dynamic_handover
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