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Curriculum-Based Deep Reinforcement Learning for Adaptive Robotics: A Mini-Review
2021
International Journal of Robotic Engineering
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Recent progress in deep reinforcement learning has corroborated to its potential to train such autonomous and robust agents. ...
At the same time, the introduction of curriculum learning has made the reinforcement learning process significantly more efficient and allowed for training on much broader tasks. ...
Breyer M, Furrer F, Novkovic T, Siegwart R, Nieto J (2018) Flexible robotic grasping with sim-to-real transfer based reinforcement learning. 25. ...
doi:10.35840/2631-5106/4131
fatcat:tnoa4vd4yrgnpjzesxr5a3jq2m
Deep-Reinforcement-Learning-Based Semantic Navigation of Mobile Robots in Dynamic Environments
[article]
2020
arXiv
pre-print
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On this account, we propose a reinforcement learning based local navigation system which learns navigation behavior based solely on visual observations to cope with highly dynamic environments. ...
Furthermore, for safety reasons, they often rely on hand-crafted safety guidelines, which makes the system less flexible and slow. ...
Training Algorithm The basic training loop is based on the suggestions from [17] and employs deep Q-learning. ...
arXiv:2008.00516v1
fatcat:gnemrlpn4zckriarj4z6bnn7zi
A Novel Sample-efficient Deep Reinforcement Learning with Episodic Policy Transfer for PID-Based Control in Cardiac Catheterization Robots
[article]
2021
arXiv
pre-print
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In this study, a sample-efficient deep reinforcement learning with episodic policy transfer is, for the first time, used for motion control during robotic catheterization with fully adaptive PID tuning ...
Robotic catheterization is typically used for percutaneous coronary intervention procedures nowadays and it involves steering flexible endovascular tools to open up occlusion in the coronaries. ...
The deep Q-network learns from a state-action space using source and target networks trained on experience replay trained on a vector of given state, action output, next state, and network parameters, ...
arXiv:2110.14941v1
fatcat:incjnx6x3vevxfujn6z2daxwdy
Position Control of Cable-Driven Robotic Soft Arm Based on Deep Reinforcement Learning
2020
Information
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deep Q learning. ...
In order to solve slow convergence and unstable effect in the process of simulation and migration when deep reinforcement learning is applied to the actual robot control task, a control strategy learning ...
The above research focuses on the control method of the model-based soft robot arm, datadriven modeling method and reinforcement learning method of the flexible manipulator. ...
doi:10.3390/info11060310
fatcat:ottk7kpaare4nhhe3biuylddla
Correction: Bhagat, S.; et al. Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges. Robotics 2019, 8, 4
2019
Robotics
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The manuscript will be updated and the original will remain online on the article webpage, with a reference to this Correction. ...
reinforcement learning (DRL) and imitation learning techniques presently occur. ...
In Figure 7 of this paper [1] , the caption was revised with the permission from the publishers as "Soft Robot Simulation on SOFA using Soft-robotics toolkit. ...
doi:10.3390/robotics8040093
fatcat:q6eojc2psbatznl3rp4v5cz4ze
Deep Reinforcement Learning for Formation Control
2018
2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)
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Continuing our work on using reinforcement learning for formation control, we present an end-to-end deep learning system which uses only camera images to learn to control the individual system's correct ...
This published work inspired us to employ a similar approach for processing the camera images and controlling the robot. We repeat the same experiment with two completely different camera positions. ...
Inspired by the work of Mnih et al. ([4] , [5] ), we present an end-to-end deep reinforcement learning approach for formation control that does not depend on specific distance sensors, but uses conventional ...
doi:10.1109/roman.2018.8525765
dblp:conf/ro-man/AykinKD18
fatcat:sam5kytiefbyncvusjpr3hp2te
Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges
2019
Robotics
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For soft robotic structures possessing countless degrees of freedom, it is at times not convenient to formulate mathematical models necessary for training a deep reinforcement learning (DRL) agent. ...
Deploying current imitation learning algorithms on soft robotic systems has provided competent results. ...
The learning from experts' actions (generally a person for soft robotic manipulation problems) can sometimes lead to less-optimal solutions while using deep neural networks to train reinforcement learning ...
doi:10.3390/robotics8010004
fatcat:bxlbnniiwrdbhnf3vghkhiew3q
Learning Mobile Manipulation through Deep Reinforcement Learning
2020
Sensors
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This paper investigates how to leverage deep reinforcement learning to tackle whole-body mobile manipulation tasks in unstructured environments using only on-board sensors. ...
It has an efficient framework decoupling visual perception from the deep reinforcement learning control, which enables its generalization from simulation training to real-world testing. ...
; • picking the object up using the policy generated by deep reinforcement learning. ...
doi:10.3390/s20030939
pmid:32050678
pmcid:PMC7039391
fatcat:yg3pdjoosrathioxsg3hbywp44
Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation
[article]
2018
arXiv
pre-print
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However, standard reinforcement learning approaches learn separate task-specific policies and assume the reward function for each task is known a priori. ...
We show that a simulated robotic car and a real-world RC car can gather data and train fully autonomously without any human-provided labels beyond those needed to train the detectors, and then at test-time ...
We propose a generalization of the reinforcement learning framework that combines flexible multitask learning, off-policy training, and the ability to learn directly from real-world events that can be ...
arXiv:1810.07167v1
fatcat:3mjsz4z2tjbmzhyjd2repvnllq
Applications and Challenges of Deep Reinforcement Learning in Multi-robot Path Planning
2021
Journal of Electronic Research and Application
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Path planning in a collision-free environment is essential for many robots to do tasks quickly and efficiently, and path planning for multiple robots using deep reinforcement learning is a new research ...
With the rapid advancement of deep reinforcement learning (DRL) in multi-agent systems, a variety of practical application challenges and solutions in the direction of multi-agent deep reinforcement learning ...
Combining model-based algorithms and model-free deep reinforcement learning path planning problems is one of the key ways to improve the efficiency of reinforcement learning in the future. ...
doi:10.26689/jera.v5i6.2809
fatcat:ohkwlmyzlrdihpzxpwbufke6ni
Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control
[article]
2015
arXiv
pre-print
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We build upon the success of recent deep reinforcement learning and develop a system for learning target reaching with a three-joint robot manipulator using external visual observation. ...
A Deep Q Network (DQN) was demonstrated to perform target reaching after training in simulation. ...
Acknowledgements This research was conducted by the Australian Research Council Centre of Excellence for Robotic Vision (project number CE140100016). ...
arXiv:1511.03791v2
fatcat:fiuwfb6dmnerrnzm7pmr4jkn2u
Transfer Learning with Demonstration Forgetting for Robotic Manipulator
2021
Procedia Computer Science
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Deep learning and especially deep reinforcement learning usually require huge amount of data for training and simulators using is perspective approach to provide this data. ...
Abstract Deep learning and especially deep reinforcement learning usually require huge amount of data for training and simulators using is perspective approach to provide this data. ...
Deep Reinforcement Learning (DRL) is perspective approach for robotic control because it has shown good results in different control problems [1, 2, 3] . ...
doi:10.1016/j.procs.2021.04.159
fatcat:ga457ocfizfijldoz3siedyatm
Motion planning in human robot cooperation via deep reinforcement learning
2019
Zenodo
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An approach to motion planning for human robot cooperation based on Deep Reinforcement Learning in simulated environments is proposed. ...
This approach aims at solving some of the typical problems of motion planning in human robot cooperation such as the need of inferring the human movements or the need of continuous re-planning trajectories ...
Currently, Deep Reinforcement Learning (DRL) is a hot topic in the field of machine learning and also with a number of applications in robotics. ...
doi:10.5281/zenodo.4795818
fatcat:ckdv2rsfxvbpvkrmnty6t7ygpe
Malleable Agents for Re-Configurable Robotic Manipulators
[article]
2022
arXiv
pre-print
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While deep reinforcement learning has had immense success in robotic manipulation, domain adaptation is a significant problem that limits its applicability to real-world robotics. ...
Re-configurable robots potentially have more utility and flexibility for many real-world tasks. Designing a learning agent to operate such robots requires adapting to different configurations. ...
Deep reinforcement learning for robotic manipulation Robotic arm manipulation tasks such as reach, pick-andplace, grasping have become a standard benchmark [27] for testing deep reinforcement learning ...
arXiv:2202.02395v1
fatcat:5imnqodlh5gjpmtq74kmy2dib4
Contact-Rich Manipulation of a Flexible Object based on Deep Predictive Learning using Vision and Tactility
[article]
2022
arXiv
pre-print
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The results of experiments using a real robot arm revealed that our method can realize motions responding to the deformation of the bag while reducing the load on the zipper. ...
Additionally, the flexible fabric bag state constantly changes during operation, so the robot needs to dynamically respond to the change. ...
ACKNOWLEDGEMENT AI Bridging Cloud Infrastructure of National Institute of Advanced Industrial Science and Technology was used. ...
arXiv:2112.06442v2
fatcat:3vhzx5ftk5duxidfceioa3hjtu
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