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Reinforcement Learning Algorithms in Humanoid Robotics
[chapter]
2007
Humanoid Robots: New Developments
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In spite of a significant progress and accomplishments achieved in the design of a hardware platform of humanoid robot and synthesis of advanced intelligent control of humanoid robots, a lot of work has ...
Finally, one ought to point out that the problem of motion of humanoid robots is a very complex control task, especially when the real environment is taken into account, requiring as a minimum, its integration ...
Aleksandar Rodi for generation of experimental data and realization of humanoid robot modeling and trajectory generation software. ...
doi:10.5772/4878
fatcat:brp43tnj35c2lmalaawtv5gv6u
Benchmarking Deep Reinforcement Learning for Continuous Control
[article]
2016
arXiv
pre-print
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We report novel findings based on the systematic evaluation of a range of implemented reinforcement learning algorithms. ...
Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning. ...
Acknowledgements We thank Emo Todorov and Yuval Tassa for providing the MuJoCo simulator, and Sergey Levine, Aviv Tamar, Chelsea Finn, and the anonymous ICML reviewers for insightful comments. ...
arXiv:1604.06778v3
fatcat:lceiaesdbvallnt57nbxp7b42q
Emergence of human-comparable balancing behaviours by deep reinforcement learning
2017
2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)
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This paper presents a hierarchical framework based on deep reinforcement learning that learns a diversity of policies for humanoid balance control. ...
The successful emergence of humanlike behaviors through deep reinforcement learning proves the feasibility of using an AI-based approach for learning humanoid balancing control in a unified framework. ...
Researchers in the computer science and robotics community have published a few papers on using deep reinforcement learning for humanoid motion control. ...
doi:10.1109/humanoids.2017.8246900
dblp:conf/humanoids/YangKL17
fatcat:zak7o5wsfvbujk6tflxawqqnee
SURVEY OF INTELLIGENT CONTROL ALGORITHMS FOR HUMANOID ROBOTS
2005
IFAC Proceedings Volumes
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algorithms) in the area of humanoid robotic systems. ...
This paper focusses on the application of intelligent control techniques (neural networks, fuzzy logic and genetic algorithms) and their hybrid forms (neuro-fuzzy networks, neuro-genetic and fuzzy-genetic ...
In this case, the gait synthesizer with reinforcement learning is based on a modified GARIC (Generalized Approximate Reasoning for Intelligent Control) method. ...
doi:10.3182/20050703-6-cz-1902.01276
fatcat:wrzes2vitbf4rkkfn3c6evxuxe
Emergence of Human-comparable Balancing Behaviors by Deep Reinforcement Learning
[article]
2018
arXiv
pre-print
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This paper presents a hierarchical framework based on deep reinforcement learning that learns a diversity of policies for humanoid balance control. ...
The successful emergence of human-like behaviors through deep reinforcement learning proves the feasibility of using an AI-based approach for learning humanoid balancing control in a unified framework. ...
Researchers in the computer science and robotics community have published a few papers on using deep reinforcement learning for humanoid motion control. ...
arXiv:1809.02074v1
fatcat:255popwpubgubocofmlakqrzve
Obtaining Humanoid Robot Controller Using Reinforcement Learning
[chapter]
2007
Humanoid Robots: New Developments
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We therefore apply reinforcement learning for the control of humanoid robots because this process resembles a human's trial and error learning process. ...
They are suitable for off-line learning, but not adequate for on-line learning. ...
Obtaining Humanoid Robot Controller Using Reinforcement Learning, Humanoid Robots: New Developments, Armando Carlos de Pina Filho (Ed.), ISBN: 978-3-902613-00-4, InTech, Available from: http://www.intechopen.com ...
doi:10.5772/4877
fatcat:riaschf3rzbb3n3t55zw7mdzfy
Recent Advances in Deep Reinforcement Learning Applications for Solving Partially Observable Markov Decision Processes (POMDP) Problems: Part 1—Fundamentals and Applications in Games, Robotics and Natural Language Processing
2021
Machine Learning and Knowledge Extraction
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The first part of a two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (DRL) applications for solving partially observable Markov decision processes (POMDP) problems ...
In this overview, we introduce Markov Decision Processes (MDP) problems and Reinforcement Learning and applications of DRL for solving POMDP problems in games, robotics, and natural language processing ...
[114] presented a comprehensive survey for learning control in robotics from reinforce to imitation in 2018. ...
doi:10.3390/make3030029
fatcat:u3y7bqkoljac5not2eq5konnnm
ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots
2022
IEEE Robotics and Automation Letters
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trajectories for humanoid robots. ...
Recently, research in computer graphics investigated machinelearning methods for character animation based on training human-like models directly on motion capture data. ...
Another recent research stream focuses on reinforcement learning (RL) for dynamic character control. ...
doi:10.1109/lra.2022.3141658
fatcat:275cp35vtbeezau3iwa3nwtoey
Practical bipedal walking control on uneven terrain using surface learning and push recovery
2011
2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
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We use a physically realistic simulation with an articulated robot model and reinforcement learning algorithm to train the push recovery controller, and implement the learned controller on a commercial ...
DARwIn-OP small humanoid robot. ...
In our former works, we have suggested practical methods to handle each problems for generic humanoid robots [20] , [21] . ...
doi:10.1109/iros.2011.6048731
fatcat:yymaa7qtznhv3lhm33jfxlnxkq
Practical bipedal walking control on uneven terrain using surface learning and push recovery
2011
2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
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We use a physically realistic simulation with an articulated robot model and reinforcement learning algorithm to train the push recovery controller, and implement the learned controller on a commercial ...
DARwIn-OP small humanoid robot. ...
In our former works, we have suggested practical methods to handle each problems for generic humanoid robots [20] , [21] . ...
doi:10.1109/iros.2011.6095131
dblp:conf/iros/YiZHL11
fatcat:zlylppy6nze6ddpc3emn3syn4a
Robust Dynamic Locomotion via Reinforcement Learning and Novel Whole Body Controller
[article]
2017
arXiv
pre-print
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Our locomotion strategy relies on devising a reinforcement learning (RL) approach for robust walking. ...
We propose a robust dynamic walking controller consisting of a dynamic locomotion planner, a reinforcement learning process for robustness, and a novel whole-body locomotion controller (WBLC). ...
ACKNOWLEDGMENT The authors would like to thank the members of the Human Centered Robotics Laboratory at The University of Texas at Austin for their great help and support. ...
arXiv:1708.02205v1
fatcat:w3ltjoku6fg6nkul35263iq3o4
Hierarchical Skills for Efficient Exploration
[article]
2021
arXiv
pre-print
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In previous work on continuous control, the sensitivity of methods to this trade-off has not been addressed explicitly, as locomotion provides a suitable prior for navigation tasks, which have been of ...
In our experiments, we show that our approach performs this trade-off effectively and achieves better results than current state-of-the-art methods for end- to-end hierarchical reinforcement learning and ...
Acknowledgements: We thank Alessandro Lazaric for insightful discussions, and Franziska Meier, Ludovic Denoyer, and Kevin Lu for helpful feedback on early versions of this paper. ...
arXiv:2110.10809v1
fatcat:26d3laqcrzc45k6omuoskbzife
Reward-Adaptive Reinforcement Learning: Dynamic Policy Gradient Optimization for Bipedal Locomotion
[article]
2021
arXiv
pre-print
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In this work, we propose a novel reward-adaptive reinforcement learning for biped locomotion, allowing the control policy to be simultaneously optimized by multiple criteria using a dynamic mechanism. ...
Recent works have demonstrated the effectiveness of deep reinforcement learning (DRL) for simulation and physical robots. ...
control for biped robot via ddpg-based deep reinforcement learning,” in
[9] K. Zhang, Z. Hou, C. W. de Silva, H. Yu, and C. ...
arXiv:2107.01908v2
fatcat:ey2t3vncprayzmvh2h3otk7hru
Robust Biped Locomotion Using Deep Reinforcement Learning on Top of an Analytical Control Approach
[article]
2021
arXiv
pre-print
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Furthermore, a learning framework is developed based on Genetic Algorithm (GA) and Proximal Policy Optimization (PPO) to find the optimum parameters and to learn how to improve the stability of the robot ...
This paper proposes a modular framework to generate robust biped locomotion using a tight coupling between an analytical walking approach and deep reinforcement learning. ...
Combination of CPG-ZMP based walking and ML algorithms Massah et al. [30] developed a hybrid CPG-ZMP controller to generate stable locomotion for humanoid robots. ...
arXiv:2104.10592v1
fatcat:ulkxmgotivdsrjdnysqimer7b4
Special Feature on Bio-Inspired Robotics
2018
Applied Sciences
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Acknowledgments: This special issue receives technical support by IEEE Robotics and Automation Society (RAS) Technical Committee on Neuro-Robotics Systems, and Technical Committee on Cyborg and Bionic ...
Systems and IEEE Systems, Man, and Cybernetics Society (SMC) Technical Committee on Bio-Mechatronics and Bio-Robotics Systems. ...
They develop an angular momentum control method based on human motion for a humanoid upper body. ...
doi:10.3390/app8050817
fatcat:umcpvvryxbfnvlmwvlcmsynj3q
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