A Fuzzy Reinforcement Learning Approach for Continuum Robot Control

Goharimanesh, Masoud; Mehrkish, Ali; Janabi–Sharifi, Farrokh

doi:10.1007/s10846-020-01237-6

Cited by 42 publications

(12 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To get the controller output within the defined pressure range, normalization of P is done. Therefore, the pneumatic The input actuation pressures to the manipulator through the low-level controller are updated by using (11). This lowlevel controller is in-built with Robotino-XT and cannot be modified or over-ridden.…”

Section: Kinematic Control Of Bcm a Kinematic Saturationmentioning

confidence: 99%

See 1 more Smart Citation

Joint-Space Kinematic Control of a Bionic Continuum Manipulator in Real-Time by Using Hybrid Approach

2022

View full text Add to dashboard Cite

Section: Kinematic Control Of Bcm a Kinematic Saturationmentioning

confidence: 99%

“…Despite several advantages, these under-actuated robots suffer control inaccuracies [11]. The nonlinear material behavior such as hysteresis, especially under large strain, compromises repeatability and adds complexity to the interpretation and prediction of the system behavior [12].…”

Section: Introductionmentioning

confidence: 99%

Joint-Space Kinematic Control of a Bionic Continuum Manipulator in Real-Time by Using Hybrid Approach

2022

View full text Add to dashboard Cite

“…Investigations on reinforcement learning for continuum robot arms using models include: research on reaching and tracking using model-based reinforcement learning methods ( Huang et al, 2018 ; Thuruthel et al, 2019 ) using guided policy search ( Levine and Koltun, 2013 ); research using genetic algorithm ( Goharimanesh et al, 2020 ); and research using a model-free reinforcement learning algorithm that learns and internally uses a forward model ( Centurelli et al, 2022 ). Model-based reinforcement learning is feasible to some extent for continuum robot arms, which can be modeled and are relatively simple in structure and materials used.…”

Section: Related Workmentioning

confidence: 99%

Characterization of continuum robot arms under reinforcement learning and derived improvements

et al. 2022

View full text Add to dashboard Cite

In robotics, soft continuum robot arms are a promising prospect owing to their redundancy and passivity; however, no comprehensive study exists that examines their characteristics compared to rigid manipulators. In this study, we examined the advantages of a continuum robot arm as compared to a typical and rigid seven-degree-of-freedom (7-DoF) robot manipulator in terms of performing various tasks through reinforcement learning. We conducted simulations for tasks with different characteristics that require control over position and force. Common tasks in robot manipulators, such as reaching, crank rotation, object throwing, and peg-in-hole were considered. The initial conditions of the robot and environment were randomized, aiming for evaluations including robustness. The results indicate that the continuum robot arm excels in the crank-rotation task, which is characterized by uncertainty in environmental conditions and cumulative rewards. However, the rigid robot arm learned better motions for the peg-in-hole task than the other tasks, which requires fine motion control of the end-effector. In the throwing task, the continuum robot arm scored well owing to its good handling of anisotropy. Moreover, we developed a reinforcement-learning method based on the comprehensive experimental results. The proposed method successfully improved the motion learning of a continuum robot arm by adding a technique to regulate the initial state of the robot. To the best of our knowledge, ours is the first reinforcement-learning experiment with multiple tasks on a single continuum robot arm and is the first report of a comparison between a single continuum robot arm and rigid manipulator on a wide range of tasks. This simulation study can make a significant contribution to the design of continuum arms and specification of their applications, and development of control and reinforcement learning methods.

show abstract

“…20 Initially, the computational agents would not have any knowledge about the optimal policy, but they learn the same by interacting with the computational environment. 21,22 The skills acquired by the computational agents can be evaluated by using the intrinsic motivations in the RL framework. 23 This optimal policy learned by the computational agent can be transferred to the robots for its optimal locomotion.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning based path planning of multiple agents of SwarmItFIX robot for fixturing operation in sheetmetal milling process

Veeramani

Muthuswamy

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

SwarmItFIX (self-reconfigurable intelligent swarm fixtures) is a multi-agent setup mainly used as a robotic fixture for large Sheet metal machining operations. A Constraint Satisfaction Problem (CSP) based planning model is utilized currently for computing the locomotion sequence of multiple agents of the SwarmItFIX. But the SwarmItFIX faces several challenges with the current planner as it fails on several occasions. Moreover, the current planner computes only the goal positions of the base agent, not the path. To overcome these issues, a novel hierarchical planner is proposed, which employs Monte Carlo and SARSA TD based model-free Reinforcement Learning (RL) algorithms for the computation of locomotion sequences of head and base agents, respectively. These methods hold two distinct features when compared with the existing methods (i) the transition model is not required for obtaining the locomotion sequence of the computational agent, and (ii) the state-space of the computational agent become scalable. The obtained results show that the proposed planner is capable of delivering optimal makespan for effective fixturing during the sheet metal milling process.

show abstract

A Fuzzy Reinforcement Learning Approach for Continuum Robot Control

Cited by 42 publications

References 44 publications

Joint-Space Kinematic Control of a Bionic Continuum Manipulator in Real-Time by Using Hybrid Approach

Joint-Space Kinematic Control of a Bionic Continuum Manipulator in Real-Time by Using Hybrid Approach

Characterization of continuum robot arms under reinforcement learning and derived improvements

Reinforcement learning based path planning of multiple agents of SwarmItFIX robot for fixturing operation in sheetmetal milling process

Contact Info

Product

Resources

About