Neural Network based Inverse Dynamics Identification and External Force Estimation on the da Vinci Research Kit

Yilmaz, Nural; Wu, Jie; Kazanzides, Peter; Tümerdem, Uğur

doi:10.1109/icra40945.2020.9197445

Cited by 53 publications

(22 citation statements)

References 24 publications

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“…To this end, we will recruit surgical trainees, record their performance, and provide one or more modalities of haptic feedback (including the tested modality of wrist-squeezing force feedback) using our new data acquisition/haptic feedback framework. Also, to one day enable the utility of our system in a real surgical setting, we will consider using external force estimation methods such as the one developed by Yilmaz et al [35]. Finally, we will upgrade the wrist-squeezing device to use a brushed DC motor instead of a servo; this will allow us to test higher fidelity force feedback methods such as those developed by Pezent et al [36].…”

Section: E Future Workmentioning

confidence: 99%

Wrist-Squeezing Force Feedback Improves Accuracy and Speed in Robotic Surgery Training

Machaca¹,

Cao²,

Chi³

et al. 2022

Preprint

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Current robotic minimally invasive surgery (RMIS) platforms provide surgeons with no haptic feedback of the robot's physical interactions. This limitation forces surgeons to rely heavily on visual feedback and can make it challenging for surgical trainees to manipulate tissue gently. Prior research has demonstrated that haptic feedback can increase task accuracy in RMIS training. However, it remains unclear whether these improvements represent a fundamental improvement in skill, or if they simply stem from re-prioritizing accuracy over task completion time. In this study, we provide haptic feedback of the force applied by the surgical instruments using custom wristsqueezing devices. We hypothesize that individuals receiving haptic feedback will increase accuracy (produce less force) while increasing their task completion time, compared to a control group receiving no haptic feedback. To test this hypothesis, N=21 novice participants were asked to repeatedly complete a ring rollercoaster surgical training task as quickly as possible. Results show that participants receiving haptic feedback apply significantly less force (0.67 N) than the control group, and they complete the task no faster or slower than the control group after twelve repetitions. Furthermore, participants in the feedback group decreased their task completion times significantly faster (7.68%) than participants in the control group (5.26%). This form of haptic feedback, therefore, has the potential to help trainees improve their technical accuracy without compromising speed.

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Section: E Future Workmentioning

confidence: 99%

Wrist-Squeezing Force Feedback Improves Accuracy and Speed in Robotic Surgery Training

Machaca¹,

Cao²,

Chi³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The identification of the kinematic and dynamic properties of the robotic arms were addressed in [43] and [216] for external forces estimation, in [57] to know the kinematics of each link instead of the serial chain, and in [59], where an entire open source package was released with the capability of modeling all the tendon couplings, springs, and counterweights.…”

Section: Parameterizationmentioning

confidence: 99%

Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit

D'Ettorre

Mariani

Stilli

et al. 2021

IEEE Robot. Automat. Mag.

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This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.R obotic-assisted surgery is now well established in clinical practice and has become the gold-standard clinical treatment option for several clinical indications. The field of robotic-assisted surgery is expected to grow substantially in the next decade, with a range of new robotic devices emerging to address unmet clinical needs across different specialties. A vibrant surgical robotics research community is pivotal for conceptualizing such new systems as well as for developing and training the engineers and scientists to translate them into practice. The da Vinci Research Kit (dVRK), an academic and industry collaborative effort to repurpose decommissioned da Vinci surgical systems [Intuitive Surgical Inc. (ISI), California, USA] as a research platform for surgical robotics research, has been a key initiative for addressing a barrier to entry for new research groups in surgical robotics. In this article, we present an extensive review of the publications that have been facilitated by the dVRK over the past decade. We classify research efforts into different categories and outline some of the major challenges and needs for the robotics community to maintain and build upon this initiative.

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“…Neural networks represent a promising approach to force estimation, replacing the need for explicit model specification with the need for more data. This approach has been used to model the dynamics of the robot in free space to predict external joint torques during environment interaction using only robot state inputs through supervised [8] or self-supervised learning [9]. Other methods attempt to use both vision-and state-based inputs to estimate forces, with many architectures also incorporating sequential temporal inputs either through recurrent neural networks [10]- [12] or a transformer network [13].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Real-time Haptic Feedback from Multimodal Neural Network-based Force Estimates during Teleoperation

Chua¹,

Okamura²

2021

Preprint

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Force estimation using neural networks is a promising approach to enable haptic feedback in minimally invasive surgical robots without end-effector force sensors. Various network architectures have been proposed, but none have been tested in real-time with surgical-like manipulations. Thus, questions remain about the real-time transparency and stability of force feedback from neural network-based force estimates. We characterize the real-time impedance transparency and stability of force feedback rendered on a da Vinci Research Kit teleoperated surgical robot using neural networks with vision-only, state-only, or state and vision inputs. Networks were trained on an existing dataset of teleoperated manipulations without force feedback. We measured real-time transparency without rendered force feedback by commanding the patient-side robot to perform vertical retractions and palpations on artificial silicone tissue. To measure stability and transparency during teleoperation with force feedback to the operator, we modeled a one-degree-of-freedom human and surgeon-side manipulandum that moved the patient-side robot to perform manipulations. We found that the multimodal vision and state network displayed more transparent impedance than single-modality networks under no force feedback. Statebased networks displayed instabilities during manipulation with force feedback. This instability was reduced in the multimodal network when refit with additional data collected during teleoperation with force feedback.

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Neural Network based Inverse Dynamics Identification and External Force Estimation on the da Vinci Research Kit

Cited by 53 publications

References 24 publications

Wrist-Squeezing Force Feedback Improves Accuracy and Speed in Robotic Surgery Training

Wrist-Squeezing Force Feedback Improves Accuracy and Speed in Robotic Surgery Training

Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit

Characterization of Real-time Haptic Feedback from Multimodal Neural Network-based Force Estimates during Teleoperation

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