A novel MPC approach to optimize force feedback for human-robot shared control

Safavi, Ali Akbar; Huynh, Loi; Rahmat-Khah, Hadi; Zahedi, Ehsan; Zadeh, Mehrdad Hosseini

doi:10.1109/iros.2015.7353793

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…Statistical algorithms such as HMM and CRF have been used to extract the aforementioned features (Reiley and Hager, 2009 ; Ramón Medina et al, 2012 ; Tao et al, 2013 ; Zahedi et al, 2017 , 2019 ). In addition, control algorithms have been applied to the human-in-the-loop system for guiding human via robots based on the model obtained from dynamic data (Chipalkatty et al, 2011 ; Safavi and Zadeh, 2015 , 2017 ; Safavi et al, 2015 ). However, previous works have an impressive progress in modeling the dynamic data in the skill transfer system, advent of machine learning and deep learning algorithms is thought as a gigantic step toward developing more trustworthy predictive systems.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Haptic Guidance for Surgical Skills Transfer

2021

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Having a trusted and useful system that helps to diminish the risk of medical errors and facilitate the improvement of quality in the medical education is indispensable. Thousands of surgical errors are occurred annually with high adverse event rate, despite inordinate number of devised patients safety initiatives. Inadvertently or otherwise, surgeons play a critical role in the aforementioned errors. Training surgeons is one of the most crucial and delicate parts of medical education and needs more attention due to its practical intrinsic. In contrast to engineering, dealing with mortal alive creatures provides a minuscule chance of trial and error for trainees. Training in operative rooms, on the other hand, is extremely expensive in terms of not only equipment but also hiring professional trainers. In addition, the COVID-19 pandemic has caused to establish initiatives such as social distancing in order to mitigate the rate of outbreak. This leads surgeons to postpone some non-urgent surgeries or operate with restrictions in terms of safety. Subsequently, educational systems are affected by the limitations due to the pandemic. Skill transfer systems in cooperation with a virtual training environment is thought as a solution to address aforesaid issues. This enables not only novice surgeons to enrich their proficiency but also helps expert surgeons to be supervised during the operation. This paper focuses on devising a solution based on deep leaning algorithms to model the behavior of experts during the operation. In other words, the proposed solution is a skill transfer method that learns professional demonstrations using different effective factors from the body of experts. The trained model then provides a real-time haptic guidance signal for either instructing trainees or supervising expert surgeons. A simulation is utilized to emulate an operating room for femur drilling surgery, which is a common invasive treatment for osteoporosis. This helps us with both collecting the essential data and assessing the obtained models. Experimental results show that the proposed method is capable of emitting guidance force haptic signal with an acceptable error rate.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Haptic Guidance for Surgical Skills Transfer

2021

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“…Alternative approaches consider a combination of learning-based MPC with a learned policy, or approximate MPC, where the former is applied offline to train the latter for online deployment. Safavi et al (2015) employed a combination of a multi-layer perceptron dynamic robot model and MPC to optimize the force feedback guiding the point-to-point movement of a manipulator assisted by a human operator. Bonzanini et al (2021) proposed an approximate multi-stage MPC framework accounting for time-varying uncertainties and model-plant mismatch.…”

Section: Introductionmentioning

confidence: 99%

Vision-Guided MPC for Robotic Path Following Using Learned Memory-Augmented Model

Rastegarpanah

Hathaway

2021

Front. Robot. AI

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The control of the interaction between the robot and environment, following a predefined geometric surface path with high accuracy, is a fundamental problem for contact-rich tasks such as machining, polishing, or grinding. Flexible path-following control presents numerous applications in emerging industry fields such as disassembly and recycling, where the control system must adapt to a range of dissimilar object classes, where the properties of the environment are uncertain. We present an end-to-end framework for trajectory-independent robotic path following for contact-rich tasks in the presence of parametric uncertainties. We formulate a combination of model predictive control with image-based path planning and real-time visual feedback, based on a learned state-space dynamic model. For modeling the dynamics of the robot-environment system during contact, we introduce the application of the differentiable neural computer, a type of memory augmented neural network (MANN). Although MANNs have been as yet unexplored in a control context, we demonstrate a reduction in RMS error of ∼21.0% compared with an equivalent Long Short-Term Memory (LSTM) architecture. Our framework was validated in simulation, demonstrating the ability to generalize to materials previously unseen in the training dataset.

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Human-Robot Collaborative Manipulation with the Suppression of Human-caused Disturbance

Wang

Zhang

2021

J Intell Robot Syst

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A novel MPC approach to optimize force feedback for human-robot shared control

Cited by 5 publications

References 19 publications

Deep Learning-Based Haptic Guidance for Surgical Skills Transfer

Deep Learning-Based Haptic Guidance for Surgical Skills Transfer

Vision-Guided MPC for Robotic Path Following Using Learned Memory-Augmented Model

Human-Robot Collaborative Manipulation with the Suppression of Human-caused Disturbance

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