Open Loop Position Control of Soft Continuum Arm Using Deep Reinforcement Learning

Satheeshbabu, Sreeshankar; Uppalapati, Naveen Kumar; Chowdhary, Girish; Krishnan, Girish

doi:10.31224/osf.io/n7h9y

Cited by 17 publications

(23 citation statements)

References 4 publications

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“…Model-free methods offer an alternative for analyzing the behavior of such complex systems without the need for elaborate modeling techniques. In our recent work [77] we presented preliminary, yet promising results on the use of Reinforcement Learning (RL) for position control of the BR 2 soft arm. The main benefit of RL over other neuro-adaptive control strategies [78][79][80] is that RL directly learns an optimal policy from experience.…”

Section: Example Results With Deep Reinforcement Learning For Soft Romentioning

confidence: 99%

Soft Robotics as an Enabling Technology for Agroforestry Practice and Research

Chowdhary

Gazzola²,

Krishnan³

et al. 2019

Sustainability

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The shortage of qualified human labor is a key challenge facing farmers, limiting profit margins and preventing the adoption of sustainable and diversified agroecosystems, such as agroforestry. New technologies in robotics could offer a solution to such limitations. Advances in soft arms and manipulators can enable agricultural robots that can have better reach and dexterity around plants than traditional robots equipped with hard industrial robotic arms. Soft robotic arms and manipulators can be far less expensive to manufacture and significantly lighter than their hard counterparts. Furthermore, they can be simpler to design and manufacture since they rely on fluidic pressurization as the primary mechanisms of operation. However, current soft robotic arms are difficult to design and control, slow to actuate, and have limited payloads. In this paper, we discuss the benefits and challenges of soft robotics technology and what it could mean for sustainable agriculture and agroforestry.

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Section: Example Results With Deep Reinforcement Learning For Soft Romentioning

confidence: 99%

Soft Robotics as an Enabling Technology for Agroforestry Practice and Research

Chowdhary

Gazzola²,

Krishnan³

et al. 2019

Sustainability

Self Cite

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“…Since the method requires no information about the robot or the environment a-priori, it enables control in complex scenarios, where highly complex physics-based models may have poorly observable parameters or states. It has also been shown that inverse kinematics for continuum robots may be approximated by a multilayer perceptron network ( George et al, 2017 ; Grassmann et al, 2018 ; Lai et al, 2019 ), with multi-agent reinforcement learning ( Ansari et al, 2016 ), with K-nearest neighbors and Gaussian mixture regression ( Chen and Lau, 2016 ), and with deep reinforcement learning ( Satheeshbabu et al, 2019 ). For reconfigurable robots subject to varying loads, it has been shown that classification of the load state using long short-term memory networks can substantially improve open-loop kinematic control ( Nicolai et al, 2020 ).…”

Section: Review Of the State Of The Artmentioning

confidence: 99%

On the Mathematical Modeling of Slender Biomedical Continuum Robots

Gilbert

2021

Front. Robot. AI

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The passive, mechanical adaptation of slender, deformable robots to their environment, whether the robot be made of hard materials or soft ones, makes them desirable as tools for medical procedures. Their reduced physical compliance can provide a form of embodied intelligence that allows the natural dynamics of interaction between the robot and its environment to guide the evolution of the combined robot-environment system. To design these systems, the problems of analysis, design optimization, control, and motion planning remain of great importance because, in general, the advantages afforded by increased mechanical compliance must be balanced against penalties such as slower dynamics, increased difficulty in the design of control systems, and greater kinematic uncertainty. The models that form the basis of these problems should be reasonably accurate yet not prohibitively expensive to formulate and solve. In this article, the state-of-the-art modeling techniques for continuum robots are reviewed and cast in a common language. Classical theories of mechanics are used to outline formal guidelines for the selection of appropriate degrees of freedom in models of continuum robots, both in terms of number and of quality, for geometrically nonlinear models built from the general family of one-dimensional rod models of continuum mechanics. Consideration is also given to the variety of actuators found in existing designs, the types of interaction that occur between continuum robots and their biomedical environments, the imposition of constraints on degrees of freedom, and to the numerical solution of the family of models under study. Finally, some open problems of modeling are discussed and future challenges are identified.

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“…It was shown that model less control can achieve higher accuracy in positioning [41] the manipulator. Further, researchers used goal babbling technique [42], RNN based control [43], [44], reinforcement learning [45], deep reinforcement learning [46] for it. However, these models suffer from following drawbacks.…”

Section: Introductionmentioning

confidence: 99%

“…The model needs to be trained from the scratch for the new conditions. Some model less algorithms [46] have shown good performance in noisy conditions or slightly changed conditions. However, the performance decreases as the magnitude of variation increases.…”

Section: Introductionmentioning

confidence: 99%

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A Study on Position Control of a Continuum Arm Using MAML (Model-Agnostic Meta-Learning) for Adapting Different Loading Conditions

Sahoo

Chakraborty

2022

IEEE Access

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Predicting tip positions of a spring based continuum manipulator is highly challenging due to its nonlinear deformations. External loading on the tip further deteriorates the accuracy. Model-less control has shown great success in the tip positioning. However, the model less control strategies require a large data set and considerable amount of time for the training. Performance of these controllers also deteriorates with external loads. To address these problems, this paper presents a MAML(Model-Agnostic Meta-Learning) based closed loop controller for the continuum manipulators. This controller requires relatively small amount of data to achieve the state-of-art positioning accuracy. It can also adapt to the changes due to the external loads with less than 2.5 percent of the original data. Two algorithms for the offline adaptation of the known and unknown external loading are proposed. This technique is also helpful for automatic stiffness tuning of the spring based continuum manipulators. The experimental validations have been done on the simulation environment and on the real prototype. The continuum arm used for the experimentation is a tendon based non constant curvature spring-based manipulator. The average relative positioning error for the zero loading case was found to be 3.83% on the spring based prototype . The controller was successful in bringing down the relative tip positioning error of the manipulator from 5.42% to 2.7% in the simulation environment. It also showed success in bringing down the relative tip positioning error from 7.8% to 3.96% on the real prototype. Average relative tip positioning errors below 4.27% and 4.89% have been achieved in the trajectory following tasks for the known and unknown external loading cases respectively.

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Open Loop Position Control of Soft Continuum Arm Using Deep Reinforcement Learning

Cited by 17 publications

References 4 publications

Soft Robotics as an Enabling Technology for Agroforestry Practice and Research

Soft Robotics as an Enabling Technology for Agroforestry Practice and Research

On the Mathematical Modeling of Slender Biomedical Continuum Robots

A Study on Position Control of a Continuum Arm Using MAML (Model-Agnostic Meta-Learning) for Adapting Different Loading Conditions

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