Machine Learning Algorithms in Bipedal Robot Control

Wang, Shouyi; Chaovalitwongse, Wanpracha Art; Babuška, Robert

doi:10.1109/tsmcc.2012.2186565

Cited by 83 publications

(41 citation statements)

References 79 publications

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“…In 2012, Kirumasi et al [23] remarked in their review that many researchers were interested in bio-inspired optimal adaptive control. Another remarkable review was done by Wang and Babuska [29] where the authors discussed several learning algorithms and their applicability to bipedal walking robots. In more recent years, [24] and [26] are published in 2017.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Systematic Review on Reinforcement Learning-Based Robotics Within the Last Decade

Khan

Tooshil

et al. 2020

IEEE Access

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Robotics is one of the many tools that is making a substantial difference as the world is experiencing the fourth industrial revolution. To ease control over this engineering marvel substantially, Reinforcement Learning (RL) has paved its way in recent years quite remarkably. RL enables robots to become self-aware towards carrying out a specific task followed by user operations. For decades of rigorous endeavor, this research field has gone through numerous groundbreaking developments and it will be the same for the coming days. Therefore, this paper steps in to enlighten the scientific community with a systemic review of the published research papers within the past decade. The bibliographic data that is extracted from the papers are analyzed using an automated tool named Vosviewer with respect to some parameters. Substantial excerpts from the most influential papers are highlighted in this work. Furthermore, this paper points out the global research practice in this field. The paper also generates some intriguing questions and answers them in regards to the research topic. After reading this paper, future researchers will have a firm idea in the RL-based robotics and will be able to incorporate in their own research.

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Section: Literature Reviewmentioning

confidence: 99%

“…Reference A (Control of robots) [23], [27], [29], [30], [72] B (Multi-agent system) [22], [20], [28] C (Different RL models) [24], [90] D (Miscellaneous) [18], [25], [89], [26], [21], [19]…”

Section: Groupmentioning

confidence: 99%

A Systematic Review on Reinforcement Learning-Based Robotics Within the Last Decade

Khan

Tooshil

et al. 2020

IEEE Access

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“…MLC is a specific application of machine learning that employs data-driven methods for control design [10][11][12][13][14]. It is also a branch of intelligent control theory which solves optimal control problems using machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Artificial Bee Colony Optimization Algorithm Incorporated With Fuzzy Theory for Real-Time Machine Learning Control of Articulated Robotic Manipulators

Huang

Chuang

2020

IEEE Access

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This paper presents a real-time machine learning control (MLC) of articulated robotic manipulators using artificial bee colony optimization (ABC) algorithm incorporated with fuzzy theory. The modified ABC with dynamic weight is used to optimize the fuzzy structure and fractional order. The fractional parameters, fuzzy membership functions and rule base are determined by means of the ABC computation. This ABC-fuzzy hybrid learning algorithm is applied to real-time MLC of robotic manipulators by including fractional order proportional-integral-derivative (FOPID) control strategy. The MLC's control gain parameters are online tuned via the ABC-fuzzy optimization. With the kinematics analysis of a sixdegree-of-freedom (DOF) articulated arm via reverse coordinates approach, an ABC-fuzzy MLC is developed to achieve motion control. A real-time operating system (RTOS) on a microprocessor collaborates with the ABC-fuzzy MLC to meet critical timing constraint by considering the dynamics of actuators. Finally, the mechatronic design and experimental setup of a six-DOF articulated robotic manipulator are constructed. Experimental results and comparative works are provided to demonstrate the merit of the proposed methods. Compared with the conventional control schemes, the proposed ABC-fuzzy MLC has theoretical and practice significance in term of real-time capability, online parameter tuning, convergent behavior and hybrid MLC. The proposed MLC methodologies are applicable to designing real-time modern controllers in both industry and academia. INDEX TERMS Artificial bee colony optimization, fuzzy theory, machine learning control, robotic arm.

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“…Nevertheless, most of these robotic solutions are based on the classical artificial intelligence paradigm relying on human design, which are limited in terms of inefficiency if robot control is highly constrained and submitted to differentiable objective functions (Rubercht et al, 2011). Classical artificial intelligence is also unable to cope with uncertainty robot behaviours (Atyabi and Powers, 2013) due to the explicit programming of desired behaviours using a complete and an exact mathematical model to conceive the robot and its environment (Wang et al, 2012). In legged robots, various learning algorithms have been proposed to provide autonomous operations for many complex and challenging control and design problems such as Tang and ER (2007).…”

Section: Introductionmentioning

confidence: 99%

An improved CUDA-based hybrid metaheuristic for fast controller of an evolutionary robot

Benalia

Djedi

Bitam

et al. 2018

IJES

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This paper proposes a novel parallel hybrid training approach to conceive an evolutionary robot. The proposed design aims to provide efficient behaviours to perform its tasks in a complex area such as walking toward a hidden destination. Embedded in robot brain, this training and evolution combination is typically accomplished by evolving considerable recurrent neural networks (RNNs) using an evolutionary strategy (ES). The effectiveness of this proposal is improved by employing CUDA technology that executes the evolutionary process of RNNs in a parallel way. The modifications applied are indicating to meet CUDA requirements in terms of CPU/GPU cooperation and memory management. Using a set of experiments performed by GPGPU-based physical simulator named open dynamics engine (ODE) and CUDA-based evolution, the effectiveness of the proposed parallel evolutionary training technique was validated for real movements of humanoid robots. This validation showed a promising speed-up, since this field requires very high powerful computational resources.

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Machine Learning Algorithms in Bipedal Robot Control

Cited by 83 publications

References 79 publications

A Systematic Review on Reinforcement Learning-Based Robotics Within the Last Decade

A Systematic Review on Reinforcement Learning-Based Robotics Within the Last Decade

Artificial Bee Colony Optimization Algorithm Incorporated With Fuzzy Theory for Real-Time Machine Learning Control of Articulated Robotic Manipulators

An improved CUDA-based hybrid metaheuristic for fast controller of an evolutionary robot

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