Relevant bibliographies by topics / Autonomous Aerial Manipulation Using a Quadrotor

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Academic literature on the topic 'Autonomous Aerial Manipulation Using a Quadrotor'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Autonomous Aerial Manipulation Using a Quadrotor"

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Zhou, De Xin, Xin Chao Ma, and Teng Da Ma. "Path Planning of Quadrotor Based on Quantum Particle Swarm Optimization Algorithm." Advanced Materials Research 760-762 (September 2013): 2018–22. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.2018.

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Nowadays, it becomes a hot research topic for autonomous flight of Quadrotor in the complex environment and the realization of fully autonomous flight is still a big challenge. The path planning of unmanned aerial vehicle is a key problem for its autonomous flight. For the path planning of Quadrotor, using the quantum particle swarm optimization algorithm, and made a lot of simulation and actual flight experiments. The results of simulation and actual flight experiment show that the using of QPSO for the path planning of Quadrotor is able to obtain a satisfactory result.
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Basri, M. A., and A. Noordin. "Optimal backstepping control of quadrotor UAV using gravitational search optimization algorithm." Bulletin of Electrical Engineering and Informatics 9, no. 5 (October 1, 2020): 1819–26. http://dx.doi.org/10.11591/eei.v9i5.2159.

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Quadrotor unmanned aerial vehicle (UAV) has superior characteristics such as ability to take off and land vertically, to hover in a stable air condition and to perform fast maneuvers. However, developing a high-performance quadrotor UAV controller is a difficult problem as quadrotor is an unstable and underactuated nonlinear system. The effort in this article focuses on designing and optimizing an autonomous quadrotor UAV controller. First, the aerial vehicle's dynamic model is presented. Then it is suggested an optimal backstepping controller (OBC). Traditionally, backstepping controller (BC) parameters are often selected arbitrarily. The gravitational search algorithm (GSA) is used here to determine the BC parameter optimum values. In the algorithm, the control parameters are calculated using an integral absolute error to minimize the fitness function. As the control law is based on the theorem of Lyapunov, the asymptotic stability of the scheme can be ensured. Finally, several simulation studies are conducted to show the efficacy of the suggested OBC.
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Jembre, Yalew Zelalem, Yuniarto Wimbo Nugroho, Muhammad Toaha Raza Khan, Muhammad Attique, Rajib Paul, Syed Hassan Ahmed Shah, and Beomjoon Kim. "Evaluation of Reinforcement and Deep Learning Algorithms in Controlling Unmanned Aerial Vehicles." Applied Sciences 11, no. 16 (August 6, 2021): 7240. http://dx.doi.org/10.3390/app11167240.

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Unmanned Aerial Vehicles (UAVs) are abundantly becoming a part of society, which is a trend that is expected to grow even further. The quadrotor is one of the drone technologies that is applicable in many sectors and in both military and civilian activities, with some applications requiring autonomous flight. However, stability, path planning, and control remain significant challenges in autonomous quadrotor flights. Traditional control algorithms, such as proportional-integral-derivative (PID), have deficiencies, especially in tuning. Recently, machine learning has received great attention in flying UAVs to desired positions autonomously. In this work, we configure the quadrotor to fly autonomously by using agents (the machine learning schemes being used to fly the quadrotor autonomously) to learn about the virtual physical environment. The quadrotor will fly from an initial to a desired position. When the agent brings the quadrotor closer to the desired position, it is rewarded; otherwise, it is punished. Two reinforcement learning models, Q-learning and SARSA, and a deep learning deep Q-network network are used as agents. The simulation is conducted by integrating the robot operating system (ROS) and Gazebo, which allowed for the implementation of the learning algorithms and the physical environment, respectively. The result has shown that the Deep Q-network network with Adadelta optimizer is the best setting to fly the quadrotor from the initial to desired position.
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XIAN, Bin. "Autonomous Control of a Micro Quadrotor Unmanned Aerial Vehicle Using Optical Flow." Journal of Mechanical Engineering 51, no. 9 (2015): 58. http://dx.doi.org/10.3901/jme.2015.09.058.

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Jiang, Tao, Defu Lin, and Tao Song. "Vision-based autonomous landing of a quadrotor using a gimbaled camera." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 14 (April 2, 2019): 5093–106. http://dx.doi.org/10.1177/0954410019837777.

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This paper proposes a novel vision-based autonomous landing scheme for micro aerial vehicles with perturbations by using a gimbaled camera. There are no sensors available on the moving target in the task. The relative position between the drone and moving target is obtained by the camera mounted on a two-degree-of-freedom pan-tile platform. Firstly, the detection algorithm runs in real time and outputs the pixel tracking errors. Then, an adaptive vision-based controller for the pan-tilt platform guarantees that the line of sight of the camera always tracks the target. Next, the relative position tracking error is approximated according to the gimbal's rotation angles. Disturbance observer-based control is applied for flight control to attenuate the effect of disturbance and recover the hign tracking performance, where relative velocity and lumped disturbances are estimated by extend disturbance observers. The proposed flight controller guarantees that the tracking errors are ultimately bounded with tunable ultimate bounds. The convergence property is demonstrated through Lyapunov theory. The simulations and experiments illustrate the effectiveness and the superiority performance of the proposed control system.
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M. Lazim, Izzuddin, Abdul Rashid Husain, Nurul Adilla Mohd Subha, and Mohd Ariffanan Mohd Basri. "Intelligent Observer-Based Feedback Linearization for Autonomous Quadrotor Control." International Journal of Engineering & Technology 7, no. 4.35 (November 30, 2018): 904. http://dx.doi.org/10.14419/ijet.v7i4.35.26280.

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The presence of disturbances can cause instability to the quadrotor flight and can be dangerous especially when operating near obstacles or other aerial vehicles. In this paper, a hybrid controller called state feedback with intelligent disturbance observer-based control (SF-iDOBC) is developed for trajectory tracking of quadrotor in the presence of time-varying disturbances, e.g. wind. This is achieved by integrating artificial intelligence (AI) technique with disturbance observer-based feedback linearization to achieve a better disturbance rejection capability. Here, the observer estimates the disturbances acting on the quadrotor, while AI technique using the radial basis function neural network (RBFNN) compensates the disturbance estimation error. To improve the error compensation of RBFNN, the k-means clustering method is used to find the optimal centers of the Gaussian activation function. In addition, the weights of the RBFNN are tuned online using the derived adaptation law based on the Lyapunov method, which eliminates the offline training. In the simulation experiment conducted, a total of four input nodes and five hidden neurons are used to compensate for the error. The results obtained demonstrate the effectiveness and merits of the theoretical development.
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Rice, Caleb, Yu Gu, Haiyang Chao, Trenton Larrabee, Srikanth Gururajan, Marcello Napolitano, Tanmay Mandal, and Matthew Rhudy. "Autonomous Close Formation Flight Control with Fixed Wing and Quadrotor Test Beds." International Journal of Aerospace Engineering 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/9517654.

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Autonomous formation flight is a key approach for reducing energy cost and managing traffic in future high density airspace. The use of Unmanned Aerial Vehicles (UAVs) has allowed low-budget and low-risk validation of autonomous formation flight concepts. This paper discusses the implementation and flight testing of nonlinear dynamic inversion (NLDI) controllers for close formation flight (CFF) using two distinct UAV platforms: a set of fixed wing aircraft named “Phastball” and a set of quadrotors named “NEO.” Experimental results show that autonomous CFF with approximately 5-wingspan separation is achievable with a pair of low-cost unmanned Phastball research aircraft. Simulations of the quadrotor flight also validate the design of the NLDI controller for the NEO quadrotors.
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Loianno, Giuseppe, Yash Mulgaonkar, Chris Brunner, Dheeraj Ahuja, Arvind Ramanandan, Murali Chari, Serafin Diaz, and Vijay Kumar. "Autonomous flight and cooperative control for reconstruction using aerial robots powered by smartphones." International Journal of Robotics Research 37, no. 11 (September 2018): 1341–58. http://dx.doi.org/10.1177/0278364918774136.

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Advances in consumer electronics products and the technology seen in personal computers, digital cameras, and smartphones phones have led to the price/performance ratio of sensors and processors falling dramatically over the last decade. In particular, many consumer products are packaged with small cameras, gyroscopes, and accelerometers, all sensors that are needed for autonomous robots in GPS-denied environments. The low mass and small form factor make them particularly well suited for autonomous flight with small flying robots. In this work, we present the first fully autonomous smartphone-based system for quadrotors. We show how multiple quadrotors can be stabilized and controlled to achieve autonomous flight in indoor buildings with application to smart homes, search and rescue, monitoring construction projects, and developing models for architecture design. In our work, the computation for sensing and control runs on an off-the-shelf smartphone, with all the software functionality embedded in a smartphone app. No additional sensors or processors are required for autonomous flight. We are also able to use multiple, coordinated autonomous aerial vehicles to improve the efficiency of our mission. In our framework, multiple vehicles are able to plan safe trajectories avoiding inter-robot collisions, while concurrently building in a cooperative manner a three-dimensional map of the environment. The work allows any consumer with any number of robots equipped with smartphones to autonomously drive a team of quadrotor robots, even without GPS, by downloading our app and cooperatively build three-dimensional maps.
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Lin, Lishan, Yuji Yang, Hui Cheng, and Xuechen Chen. "Autonomous Vision-Based Aerial Grasping for Rotorcraft Unmanned Aerial Vehicles." Sensors 19, no. 15 (August 3, 2019): 3410. http://dx.doi.org/10.3390/s19153410.

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Autonomous vision-based aerial grasping is an essential and challenging task for aerial manipulation missions. In this paper, we propose a vision-based aerial grasping system for a Rotorcraft Unmanned Aerial Vehicle (UAV) to grasp a target object. The UAV system is equipped with a monocular camera, a 3-DOF robotic arm with a gripper and a Jetson TK1 computer. Efficient and reliable visual detectors and control laws are crucial for autonomous aerial grasping using limited onboard sensing and computational capabilities. To detect and track the target object in real time, an efficient proposal algorithm is presented to reliably estimate the region of interest (ROI), then a correlation filter-based classifier is developed to track the detected object. Moreover, a support vector regression (SVR)-based grasping position detector is proposed to improve the grasp success rate with high computational efficiency. Using the estimated grasping position and the UAV?Äôs states, novel control laws of the UAV and the robotic arm are proposed to perform aerial grasping. Extensive simulations and outdoor flight experiments have been implemented. The experimental results illustrate that the proposed vision-based aerial grasping system can autonomously and reliably grasp the target object while working entirely onboard.
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Armendariz, Saul, Victor Becerra, and Nils Bausch. "Bio-inspired Autonomous Visual Vertical and Horizontal Control of a Quadrotor Unmanned Aerial Vehicle." Electronics 8, no. 2 (February 5, 2019): 184. http://dx.doi.org/10.3390/electronics8020184.

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Near-ground manoeuvres, such as landing, are key elements in unmanned aerial vehicle navigation. Traditionally, these manoeuvres have been done using external reference frames to measure or estimate the velocity and the height of the vehicle. Complex near-ground manoeuvres are performed by flying animals with ease. These animals perform these complex manoeuvres by exclusively using the information from their vision and vestibular system. In this paper, we use the Tau theory, a visual strategy that, is believed, is used by many animals to approach objects, as a solution for relative ground distance control for unmanned vehicles. In this paper, it is shown how this approach can be used to perform near-ground manoeuvres in a vertical and horizontal manner on a moving target without the knowledge of height and velocity of either the vehicle or the target. The proposed system is tested with simulations. Here, it is shown that, using the proposed methods, the vehicle is able to perform landing on a moving target, and also they enable the user to choose the dynamic characteristics of the approach.
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Dissertations / Theses on the topic "Autonomous Aerial Manipulation Using a Quadrotor"

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Ghadiok, Vaibhav. "Autonomous Aerial Manipulations Using a Quadrotor." DigitalCommons@USU, 2011. https://digitalcommons.usu.edu/etd/1034.

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This paper presents an implementation of autonomous indoor aerial gripping using a low-cost, custom-built quadrotor. Such research extends the typical functionality of micro air vehicles (MAV) from passive observation and sensing to dynamic interaction with the environment. To achieve this, three major challenges are overcome: precise positioning, sensing and manipulation of the object, and stabilization in the presence of disturbance due to interaction with the object. Navigation in both indoor and outdoor unstructured, Global Positioning System-denied (GPS-denied) environments is achieved using a visual Simultaneous Localization and Mapping (SLAM) algorithm that relies on an onboard monocular camera. A secondary camera, capable of detecting infrared light sources, is used to estimate the 3D location of the object, while an under-actuated and passively compliant manipulator is designed for effective gripping under uncertainty. The system utilizes nested ProportionalIntegral-Derivative (PID) controllers for attitude stabilization, vision-based navigation, and gripping. The quadrotor is therefore able to autonomously navigate, locate, and grasp an object, using only onboard sensors.
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Srikanth, Manohar B. (Manohar Balagatte). "Controlled manipulation using autonomous aerial systems." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/79286.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, February 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 134-135).
The main focus of the thesis is to design and control Autonomous Aerial Systems, also referred to as Unmanned Aerial Vehicles (UAVs). UAVs are able to hover and navigate in space using the thrust forces generated by the propellers. One of the simplest such vehicles that is widely used is a Quadrotor. While UAVs have been predominantly used for "fly and sense" applications, very few investigations have focused on using them to perform manipulation by contact. The latter is challenging because of the dual goal of performing manipulation and maintaining stable flight. Because Quadrotors can quickly reach a location, their ability to manipulate can be impactful in many scenarios. While efficient flight control of Quadrotor has been an active research area, using Quadrotor to perform manipulation is novel and challenging. In this thesis, a range of Quadrotor designs and control strategies are proposed in order to carry out autonomous manipulation of objects. We first derive a dynamic model of the Quadrotor that accounts for the presence of contact, object dynamics and kinematics. To improve manipulation performance, a passive light-weight end-effector interface between the Quadrotor and the object is proposed. The complexity of the dynamics is systematically reduced by making certain assumptions. The resulting dynamic model is divided into nonlinear subsystems on the basis of their degrees of freedom, for each of which separate controllers are designed. An efficient docking approach is proposed that permits fast and aggressive docking, even at very high speeds. Because a single Quadrotor UAS is limited in manipulation capability, a multi Quadrotor cooperative manipulation scheme is proposed. Control strategies are proposed to deal with kinematic and parametric uncertainties. A manipulation scheme to open a door with unknown hinge location is proposed. A nonlinear adaptive controller is implemented to perform efficient tracking in the presence of parametric uncertainty. In order to improve robustness to accidental contacts, a novel flexible Quadrotor, denoted as ParaFlex, is designed. The advantages of ParaFlex over a rigid Quadrotor are demonstrated. A Simulation, Test and Validation Environment (STeVE) is developed to facilitate smooth and efficient transition from design process to simulation to experiments.
by Manohar B. Srikanth.
Ph.D.
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Conference papers on the topic "Autonomous Aerial Manipulation Using a Quadrotor"

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Roberts, Luke, Hugh A. Bruck, and Satyandra K. Gupta. "Autonomous Loitering Control for a Flapping Wing Miniature Aerial Vehicle With Independent Wing Control." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34752.

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Flapping wing miniature aerial vehicles (FWMAVs) offer advantages over traditional fixed wing or quadrotor MAV platforms because they are more maneuverable than fixed wing aircraft and are more energy efficient than quadrotors, while being quieter than both. Currently, autonomy in FWMAVs has only been implemented in flapping vehicles without independent wing control, limiting their level of control. We have developed Robo Raven IV, a FWMAV platform with independently controllable wings and an actuated tail controlled by an onboard autopilot system. In this paper, we present the details of Robo Raven IV platform along with a control algorithm that uses a GPS, gyroscope, compass, and custom PID controller to autonomously loiter about a predefined point. We show through simulation that this system has the ability to loiter in a 50 meter radius around a predefined location through the manipulation of the wings and tail. A simulation of the algorithm using characterized GPS and tail response error via a PID controller is also developed. Flight testing of Robo Raven IV demonstrated the success of this platform, even in winds of up to 10 mph.
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Ghadiok, V., J. Goldin, and Wei Ren. "Autonomous indoor aerial gripping using a quadrotor." In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011). IEEE, 2011. http://dx.doi.org/10.1109/iros.2011.6048786.

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Ghadiok, Vaibhav, Jeremy Goldin, and Wei Ren. "Autonomous indoor aerial gripping using a quadrotor." In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011). IEEE, 2011. http://dx.doi.org/10.1109/iros.2011.6094690.

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Suseong Kim, Seungwon Choi, and H. Jin Kim. "Aerial manipulation using a quadrotor with a two DOF robotic arm." In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013). IEEE, 2013. http://dx.doi.org/10.1109/iros.2013.6697077.

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Goodarzi, Farhad A. "Autonomous aerial payload delivery with quadrotor using varying length cable." In 2016 International Conference on Advanced Mechatronic Systems (ICAMechS). IEEE, 2016. http://dx.doi.org/10.1109/icamechs.2016.7813481.

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Rawashdeh, Osamah A., Hong Chul Yang, Rami D. AbouSleiman, and Belal H. Sababha. "Microraptor: A Low-Cost Autonomous Quadrotor System." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86490.

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This paper describes Microraptor, a complete low-cost autonomous quadrotor system designed for surveillance and reconnaissance applications. The Microraptor ground station is custom-made and features a graphical user interface that presents and allows the manipulation of various flight parameters. The aerial vehicle is a 4-rotor vertical takeoff and landing (VTOL) vehicle that features the advantages of traditional helicopters with significant reduction in mechanical complexity. The vehicle frame is a handmade magnesium and carbon fiber structure. The onboard avionics system is a custom dual processor design capable of autonomous path navigation and data exchange with the ground station. The vehicle is outfitted with a video and still-photo system that provides real-time images to the system operator through the GUI. The system is being developed at Oakland University by a team of multidisciplinary undergraduate and graduate engineering students. Microraptor placed 5th at the 2008 Association for Unmanned Vehicle Systems International (AUVSI) Unmanned Aerial Systems (UAS) Competition and is set to compete again in June of 2009.
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Jiao, Ran, Mingjie Dong, Wusheng Chou, Hailong Yu, and Hao Yu. "Autonomous Aerial Manipulation Using a Hexacopter Equipped with a Robotic Arm." In 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2018. http://dx.doi.org/10.1109/robio.2018.8664845.

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Palunko, Ivana, Aleksandra Faust, Patricio Cruz, Lydia Tapia, and Rafael Fierro. "A reinforcement learning approach towards autonomous suspended load manipulation using aerial robots." In 2013 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2013. http://dx.doi.org/10.1109/icra.2013.6631276.

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Zhao, Tiebiao, Chris Currier, Alexis Bonnin, Gregory Mellos, Noe Martinez, and YangQuan Chen. "Low Cost Autonomous Battery Replacement System for Quadrotor Small Unmanned Aerial Systems (sUAS) using 3D Printing Components." In 2018 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2018. http://dx.doi.org/10.1109/icuas.2018.8453381.

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Barros, Gabriel Moraes, and Esther Colombini. "Reinforcement and Imitation Learning Applied to Autonomous Aerial Robot Control." In VIII Workshop de Teses e Dissertações em Robótica/Concurso de Teses e Dissertações em Robótica. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wtdr_ctdr.2020.14956.

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In robotics, the ultimate goal of reinforcement learning is to endow robots with the ability to learn, improve, adapt, and reproduce tasks with dynamically changing constraints based on exploration and autonomous learning. Reinforcement Learning (RL) aims at addressing this problem by enabling a robot to learn behaviors through trial-and-error. With RL, a Neural Network can be trained as a function approximator to directly map states to actuator commands making any predefined control structure not-needed for training. However, the knowledge required to converge these methods is usually built from scratch. Learning may take a long time, not to mention that RL algorithms need a stated reward function. Sometimes, it is not trivial to define one. Often it is easier for a teacher, human or intelligent agent, do demonstrate the desired behavior or how to accomplish a given task. Humans and other animals have a natural ability to learn skills from observation, often from merely seeing these skills’ effects: without direct knowledge of the underlying actions. The same principle exists in Imitation Learning, a practical approach for autonomous systems to acquire control policies when an explicit reward function is unavailable, using supervision provided as demonstrations from an expert, typically a human operator. In this scenario, this work’s primary objective is to design an agent that can successfully imitate a prior acquired control policy using Imitation Learning. The chosen algorithm is GAIL since we consider that it is the proper algorithm to tackle this problem by utilizing expert (state, action) trajectories. As reference expert trajectories, we implement state-of-the-art on and off-policy methods PPO and SAC. Results show that the learned policies for all three methods can solve the task of low-level control of a quadrotor and that all can account for generalization on the original tasks.
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