Relevant bibliographies by topics / Sim2real

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Sim2real'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2023

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Journal articles on the topic "Sim2real"

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Zhong, Chengliang, Chao Yang, Fuchun Sun, Jinshan Qi, Xiaodong Mu, Huaping Liu, and Wenbing Huang. "Sim2Real Object-Centric Keypoint Detection and Description." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (June 28, 2022): 5440–49. http://dx.doi.org/10.1609/aaai.v36i5.20482.

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Keypoint detection and description play a central role in computer vision. Most existing methods are in the form of scene-level prediction, without returning the object classes of different keypoints. In this paper, we propose the object-centric formulation, which, beyond the conventional setting, requires further identifying which object each interest point belongs to. With such fine-grained information, our framework enables more downstream potentials, such as object-level matching and pose estimation in a clustered environment. To get around the difficulty of label collection in the real world, we develop a sim2real contrastive learning mechanism that can generalize the model trained in simulation to real-world applications. The novelties of our training method are three-fold: (i) we integrate the uncertainty into the learning framework to improve feature description of hard cases, e.g., less-textured or symmetric patches; (ii) we decouple the object descriptor into two independent branches, intra-object salience and inter-object distinctness, resulting in a better pixel-wise description; (iii) we enforce cross-view semantic consistency for enhanced robustness in representation learning. Comprehensive experiments on image matching and 6D pose estimation verify the encouraging generalization ability of our method. Particularly for 6D pose estimation, our method significantly outperforms typical unsupervised/sim2real methods, achieving a closer gap with the fully supervised counterpart.
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Hofer, Sebastian, Kostas Bekris, Ankur Handa, Juan Camilo Gamboa, Melissa Mozifian, Florian Golemo, Chris Atkeson, et al. "Sim2Real in Robotics and Automation: Applications and Challenges." IEEE Transactions on Automation Science and Engineering 18, no. 2 (April 2021): 398–400. http://dx.doi.org/10.1109/tase.2021.3064065.

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Gomes, Daniel Fernandes, Paolo Paoletti, and Shan Luo. "Generation of GelSight Tactile Images for Sim2Real Learning." IEEE Robotics and Automation Letters 6, no. 2 (April 2021): 4177–84. http://dx.doi.org/10.1109/lra.2021.3063925.

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Allamaa, Jean Pierre, Panagiotis Patrinos, Herman Van der Auweraer, and Tong Duy Son. "Sim2real for Autonomous Vehicle Control using Executable Digital Twin." IFAC-PapersOnLine 55, no. 24 (2022): 385–91. http://dx.doi.org/10.1016/j.ifacol.2022.10.314.

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Liu, Wenzheng, Chun Zhao, Yue Liu, Hongwei Wang, Wei Zhao, and Heming Zhang. "Sim2real kinematics modeling of industrial robots based on FPGA-acceleration." Robotics and Computer-Integrated Manufacturing 77 (October 2022): 102350. http://dx.doi.org/10.1016/j.rcim.2022.102350.

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Weibel, Jean-Baptiste, Timothy Patten, and Markus Vincze. "Addressing the Sim2Real Gap in Robotic 3-D Object Classification." IEEE Robotics and Automation Letters 5, no. 2 (April 2020): 407–13. http://dx.doi.org/10.1109/lra.2019.2959497.

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Kadian, Abhishek, Joanne Truong, Aaron Gokaslan, Alexander Clegg, Erik Wijmans, Stefan Lee, Manolis Savva, Sonia Chernova, and Dhruv Batra. "Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance?" IEEE Robotics and Automation Letters 5, no. 4 (October 2020): 6670–77. http://dx.doi.org/10.1109/lra.2020.3013848.

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Truong, Joanne, Sonia Chernova, and Dhruv Batra. "Bi-Directional Domain Adaptation for Sim2Real Transfer of Embodied Navigation Agents." IEEE Robotics and Automation Letters 6, no. 2 (April 2021): 2634–41. http://dx.doi.org/10.1109/lra.2021.3062303.

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Zhang, Tan, Kefang Zhang, Jiatao Lin, Wing-Yue Geoffrey Louie, and Hui Huang. "Sim2real Learning of Obstacle Avoidance for Robotic Manipulators in Uncertain Environments." IEEE Robotics and Automation Letters 7, no. 1 (January 2022): 65–72. http://dx.doi.org/10.1109/lra.2021.3116700.

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Abascal, Juan F. P. J., Nicolas Ducros, Valeriya Pronina, Simon Rit, Pierre-Antoine Rodesch, Thomas Broussaud, Suzanne Bussod, et al. "Material Decomposition in Spectral CT Using Deep Learning: A Sim2Real Transfer Approach." IEEE Access 9 (2021): 25632–47. http://dx.doi.org/10.1109/access.2021.3056150.

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Dissertations / Theses on the topic "Sim2real"

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Golemo, Florian. "How to Train Your Robot. New Environments for Robotic Training and New Methods for Transferring Policies from the Simulator to the Real Robot." Thesis, Bordeaux, 2018. https://tel.archives-ouvertes.fr/tel-03177806.

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Les robots sont l’avenir. Mais comment pouvons-nous leur apprendre de nouvelles compétences utiles? Ce travail couvre une variété de sujets, ayant tous pour but commun de faciliter l’entraînement des robots. La première composante principale de cette thèse est notre travail sur le transfert de modélisation sim2real. Lorsqu’une stratégie a été entièrement apprise en simulation, ses performances sont généralement considérablement inférieures à celles du vrai robot. Cela peut être dû à du bruit aléatoire, à des imprécisions ou à des effets non modélisés, tels que des réactions en retour. Nous introduisons une nouvelle technique pour apprendre la différence entre le simulateur et le vrai robot et pour l’utiliser afin de corriger le simulateur. Nous avons constaté que pour plusieurs de nos idées, aucune simulation appropriée n’était disponible. Par conséquent, pour la deuxième partie principale de la thèse, nous avons créé un ensemble de nouvelles simulations robotiques et de nouveaux environnements de test. Nous fournissons (1) plusieurs nouvelles simulations pour des robots existants, ainsi que des variantes d’environnements existants, qui permettent un ajustement rapide de la dynamique du robot. Nous avons également co-créé (2) le défi AIDO de Duckietown, qui est un concours de robotique en direct à grande échelle pour les conférences NIPS 2018 et ICRA 2019. Pour ce défi, nous avons créé l’infrastructure de simulation, qui permet aux participants d’entraîner leurs robots en simulation avec ou sans ROS. Il leur permet également d’évaluer automatiquement leurs soumissions sur des robots en direct dans un ”Robotarium”. Afin d’évaluer la compréhension et l’acquisition continue de langage par un robot, nous avons développé le (3) Test d’Interaction Multimodal Homme-Robot (MHRI). Cet ensemble de tests contient plusieurs heures d’enregistrements annotés de différentes personnes montrant et pointant des objets ménagers courants, le tout du point de vue d’un robot. La nouveauté et la difficulté de cette tâche découlent du bruit réaliste inclus dans le jeu de données: la plupart des personnes n’était pas de langue maternelle anglaise, certains objets étaient obstrués et personne n’avait reçu d’instructions détaillées sur la manière de communiquer avec le robot, entraînant des interactions très naturelles. Nous avons constaté un manque flagrant de simulations d’environnements domestiques réalistes, avec annotations sémantiques, qui permettraient à un agent d’acquérir les compétences nécessaires pour maîtriser une telle tâche. C’est pourquoi nous avons créé (4) HoME, une plate-forme de formation de robots domestiques à la compréhension du langage. L’environnement a été créé en encapsulant la base de données existante SUNCG 3D, composée de maisons, dans un moteur de jeu pour permettre aux agents simulés de parcourir ces dernières. Il intègre un moteur acoustique très détaillé et un moteur sémantique pouvant générer des descriptions d’objets en relation avec d’autres objets, meubles et pièces. La troisième et dernière contribution principale de ce travail prend en considération le fait qu’un robot peut se trouver dans un nouvel environnement non couvert par la simulation. Dans un tel cas, nous fournissons une nouvelle approche qui permet à l’agent de reconstruire une scène 3D à partir d’une seule image 2D en apprenant l’intégration d’objets. Le principal inconvénient de ce travail est qu’il ne prend actuellement pas en charge de manière fiable la reconstruction de couleur et de texture. Nous avons testé cette approche sur une tâche de rotation mentale, courante dans les tests de QI, et avons constaté que notre modèle arrivait nettement mieux à reconnaître et à faire pivoter des objets que plusieurs modèles de référence
Robots are the future. But how can we teach them useful new skills? This work covers a variety of topics, all with the common goal of making it easier to train robots. The first main component of this thesis is our work on model-building sim2real transfer. When a policy has been learned entirely in simulation, the performance of this policy is usually drastically lower on the real robot. This can be due to random noise, to imprecisions, or to unmodelled effects like backlash. We introduce a new technique for learning the discrepancy between the simulator and the real robot and using this discrepancy to correct the simulator. We found that for several of our ideas there weren’t any suitable simulations available. Therefore, for the second main part of the thesis, we created a set of new robotic simulation and test environments. We provide (1) several new robot simulations for existing robots and variations on existing environments that allow for rapid adjustment of the robot dynamics. We also co-created (2) the Duckietown AIDO challenge, which is a large scale live robotics competition for the conferences NIPS 2018 and ICRA 2019. For this challenge we created the simulation infrastructure, which allows participants to train their robots in simulation with or without ROS. It also lets them evaluate their submissions automatically on live robots in a ”Robotarium”. In order to evaluate a robot’s understanding and continuous acquisition of language, we developed the (3) Multimodal Human-Robot Interaction benchmark (MHRI). This test set contains several hours of annotated recordings of different humans showing and pointing at common household items, all from a robot’s perspective. The novelty and difficulty in this task stems from the realistic noise that is included in the dataset: Most humans were non-native English speakers, some objects were occluded and none of the humans were given any detailed instructions on how to communicate with the robot, resulting in very natural interactions. After completing this benchmark, we realized the lack of simulation environments that are sufficiently complex to train a robot for this task. This would require an agent in a realistic house settings with semantic annotations. That is why we created (4) HoME, a platform for training household robots to understand language. The environment was created by wrapping the existing SUNCG 3D database of houses in a game engine to allow simulated agents to traverse the houses. It integrates a highly-detailed acoustic engine and a semantic engine that can generate object descriptions in relation to other objects, furniture, and rooms. The third and final main contribution of this work considered that a robot might find itself in a novel environment which wasn’t covered by the simulation. For such a case we provide a new approach that allows the agent to reconstruct a 3D scene from 2D images by learning object embeddings, since especially in low-cost robots a depth sensor is not always available, but 2D cameras a common. The main drawback of this work is that it currently doesn’t reliably support reconstruction of color or texture. We tested the approach on a mental rotation task, which is common in IQ tests, and found that our model performs significantly better in recognizing and rotating objects than several baselines
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Collins, Jack T. "Simulation to reality and back: A robot's guide to crossing the reality gap." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/230537/1/Jack_Collins_Thesis.pdf.

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Simulation is an indispensable technology within robotics; however, the reality gap prevents many simulated solutions from transferring perfectly to reality. This thesis investigates the reality gap within the context of robotic manipulation. We present studies that first quantify and then benchmark the reality gap when comparing popular robotic simulators to a real-world ground truth collected using motion capture. We then present a promising new method for overcoming the reality gap that employs an online sim-to-real approach that utilises differentiable physics to iteratively narrow the gap and improve the simulation environment using data collected from the real system.
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Bosello, Michael. "Integrating BDI and Reinforcement Learning: the Case Study of Autonomous Driving." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21467/.

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Recent breakthroughs in machine learning are paving the way to the vision of software 2.0 era, which foresees the replacement of traditional software development with such techniques for many applications. In the context of agent-oriented programming, we believe that mixing together cognitive architectures like the BDI one and learning techniques could trigger new interesting scenarios. In that view, our previous work presents Jason-RL, a framework that integrates BDI agents and Reinforcement Learning (RL) more deeply than what has been already proposed so far in the literature. The framework allows the development of BDI agents having both explicitly programmed plans and plans learned by the agent using RL. The two kinds of plans are seamlessly integrated and can be used without differences. Here, we take autonomous driving as a case study to verify the advantages of the proposed approach and framework. The BDI agent has hard-coded plans that define high-level directions while fine-grained navigation is learned by trial and error. This approach – compared to plain RL – is encouraging as RL struggles in temporally extended planning. We defined and trained an agent able to drive in a track with an intersection, at which it has to choose the correct path to reach the assigned target. A first step towards porting the system in the real-world has been done by building a 1/10 scale racecar prototype which learned how to drive in a simple track.
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Book chapters on the topic "Sim2real"

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Russel, Reazul Hasan, Mouhacine Benosman, Jeroen van Baar, and Radu Corcodel. "Lyapunov Robust Constrained-MDPs for Sim2Real Transfer Learning." In Federated and Transfer Learning, 307–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11748-0_13.

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Dimitropoulos, Konstantinos, Ioannis Hatzilygeroudis, and Konstantinos Chatzilygeroudis. "A Brief Survey of Sim2Real Methods for Robot Learning." In Advances in Service and Industrial Robotics, 133–40. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04870-8_16.

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Sahu, Manish, Ronja Strömsdörfer, Anirban Mukhopadhyay, and Stefan Zachow. "Endo-Sim2Real: Consistency Learning-Based Domain Adaptation for Instrument Segmentation." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 784–94. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59716-0_75.

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Indla, Vineela, Vennela Indla, Sai Narayanan, Akhilesh Ramachandran, Arunkumar Bagavathi, Vishalini Laguduva Ramnath, and Sathyanarayanan N. Aakur. "Sim2Real for Metagenomes: Accelerating Animal Diagnostics with Adversarial Co-training." In Advances in Knowledge Discovery and Data Mining, 164–75. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75762-5_14.

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Tabak, Jelena, Marsela Polić, and Matko Orsag. "Towards Synthetic Data: Dealing with the Texture-Bias in Sim2real Learning." In Intelligent Autonomous Systems 17, 630–42. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-22216-0_42.

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Weibel, Jean-Baptiste, Rainer Rohrböck, and Markus Vincze. "Measuring the Sim2Real Gap in 3D Object Classification for Different 3D Data Representation." In Lecture Notes in Computer Science, 107–16. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87156-7_9.

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Chen, Yiwen, Xue Li, Sheng Guo, Xian Yao Ng, and Marcelo H. Ang. "Real2Sim or Sim2Real: Robotics Visual Insertion Using Deep Reinforcement Learning and Real2Sim Policy Adaptation." In Intelligent Autonomous Systems 17, 617–29. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-22216-0_41.

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Akhmetzyanov, Aydar, Maksim Rassabin, Alexander Maloletov, Mikhail Fadeev, and Alexandr Klimchik. "Model Free Error Compensation for Cable-Driven Robot Based on Deep Learning with Sim2real Transfer Learning." In Informatics in Control, Automation and Robotics, 479–96. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92442-3_24.

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Liao, Hsuan-Cheng, Han-Jung Chou, and Jing-Sin Liu. "Velocity Planning via Model-Based Reinforcement Learning: Demonstrating Results on PILCO for One-Dimensional Linear Motion with Bounded Acceleration." In Applied Intelligence - Annual Volume 2022 [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.103690.

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The time-optimal control problem (TOCP) has faced new practical challenges, such as those from the deployment of agile autonomous vehicles in diverse uncertain operating conditions without accurate system calibration. In this study to meet a need to generate feasible speed profiles in the face of uncertainty, we exploit and implement probabilistic inference for learning control (PILCO), an existing sample-efficient model-based reinforcement learning (MBRL) framework for policy search, to a case study of TOCP for a vehicle that was modeled as a constant input-constrained double integrator with uncertain inertia subject to uncertain viscous friction. Our approach integrates learning, planning, and control to construct a generalizable approach that requires minimal assumptions (especially regarding external disturbances and the parametric dynamics model of the system) for solving TOCP approximately as the perturbed solutions close to time-optimality. Within PILCO, a Gaussian Radial basis functions is implemented to generate control-constrained rest-to-rest near time-optimal vehicle motion on a linear track from scratch with data-efficiency in a direct way. We briefly introduce the importance of the applications of PILCO and discuss the learning results that PILCO would actually converge to the analytical solution in this TOCP. Furthermore, we execute a simulation and a sim2real experiment to validate the suitability of PILCO for TOCP by comparing with the analytical solution.
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Conference papers on the topic "Sim2real"

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Sadeghi, Fereshteh, Alexander Toshev, Eric Jang, and Sergey Levine. "Sim2Real Viewpoint Invariant Visual Servoing by Recurrent Control." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00493.

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Kaspar, Manuel, Juan D. Munoz Osorio, and Juergen Bock. "Sim2Real Transfer for Reinforcement Learning without Dynamics Randomization." In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020. http://dx.doi.org/10.1109/iros45743.2020.9341260.

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Chen, Zexi, Zheyuan Huang, Hongxiang Yu, Zhongxiang Zhou, Yunkai Wang, Xuecheng Xu, Qimeng Tan, Yue Wang, and Rong Xiong. "Learn to Differ: Sim2Real Small Defection Segmentation Network." In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2021. http://dx.doi.org/10.1109/iros51168.2021.9636491.

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Zhang, John Z., Yu Zhang, Pingchuan Ma, Elvis Nava, Tao Du, Philip Arm, Wojciech Matusik, and Robert K. Katzschmann. "Sim2Real for Soft Robotic Fish via Differentiable Simulation." In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022. http://dx.doi.org/10.1109/iros47612.2022.9981338.

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Pashevich, Alexander, Robin Strudel, Igor Kalevatykh, Ivan Laptev, and Cordelia Schmid. "Learning to Augment Synthetic Images for Sim2Real Policy Transfer." In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019. http://dx.doi.org/10.1109/iros40897.2019.8967622.

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Gao, Haichuan, Zhile Yang, Xin Su, Tian Tan, and Feng Chen. "Adaptability Preserving Domain Decomposition for Stabilizing Sim2Real Reinforcement Learning." In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020. http://dx.doi.org/10.1109/iros45743.2020.9341124.

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Gao, Ruohan, Zilin Si, Yen-Yu Chang, Samuel Clarke, Jeannette Bohg, Li Fei-Fei, Wenzhen Yuan, and Jiajun Wu. "ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.01034.

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Ikeda, Takuya, Suomi Tanishige, Ayako Amma, Michael Sudano, Herve Audren, and Koichi Nishiwaki. "Sim2Real Instance-Level Style Transfer for 6D Pose Estimation." In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022. http://dx.doi.org/10.1109/iros47612.2022.9981878.

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Suh, Young-Ho, Sung-Pil Woo, Hyunhak Kim, and Dong-Hwan Park. "A sim2real framework enabling decentralized agents to execute MADDPG tasks." In the Workshop. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3366622.3368146.

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Balaji, Bharathan, Sunil Mallya, Sahika Genc, Saurabh Gupta, Leo Dirac, Vineet Khare, Gourav Roy, et al. "DeepRacer: Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learning." In 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020. http://dx.doi.org/10.1109/icra40945.2020.9197465.

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