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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.12099 |
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| _version_ | 1866914387884769280 |
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| author | Zhou, Haoying Yang, Hao Burkhart, Brendan Deguet, Anton Fichera, Loris Fischer, Gregory S. Wu, Jie Ying Kazanzides, Peter |
| author_facet | Zhou, Haoying Yang, Hao Burkhart, Brendan Deguet, Anton Fichera, Loris Fischer, Gregory S. Wu, Jie Ying Kazanzides, Peter |
| contents | The da Vinci Research Kit (dVRK) is widely used for research in robot-assisted surgery, but most modeling and control methods target the first-generation dVRK Classic. The recently introduced dVRK-Si, built from da Vinci Si hardware, features a redesigned Patient Side Manipulator (PSM) with substantially larger gravity loading, which can degrade control if unmodeled. This paper presents the first complete kinematic and dynamic modeling framework for the dVRK-Si PSM. We derive a modified DH kinematic model that captures the closed-chain parallelogram mechanism, formulate dynamics via the Euler-Lagrange method, and express inverse dynamics in a linear-in-parameters regressor form. Dynamic parameters are identified from data collected on a periodic excitation trajectory optimized for numerical conditioning and estimated by convex optimization with physical feasibility constraints. Using the identified model, we implement real-time gravity compensation and computed-torque feedforward in the dVRK control stack. Experiments on a physical dVRK-Si show that the gravity compensation reduces steady-state joint errors by 68-84% and decreases end-effector tip drift during static holds from 4.2 mm to 0.7 mm. Computed-torque feedforward further improves transient and position tracking accuracy. For sinusoidal trajectory tracking, computed-torque feedforward reduces position errors by 35% versus gravity-only feedforward and by 40% versus PID-only. The proposed pipeline supports reliable control, high-fidelity simulation, and learning-based automation on the dVRK-Si. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12099 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Towards Dynamic Model Identification and Gravity Compensation for the dVRK-Si Patient Side Manipulator Zhou, Haoying Yang, Hao Burkhart, Brendan Deguet, Anton Fichera, Loris Fischer, Gregory S. Wu, Jie Ying Kazanzides, Peter Robotics The da Vinci Research Kit (dVRK) is widely used for research in robot-assisted surgery, but most modeling and control methods target the first-generation dVRK Classic. The recently introduced dVRK-Si, built from da Vinci Si hardware, features a redesigned Patient Side Manipulator (PSM) with substantially larger gravity loading, which can degrade control if unmodeled. This paper presents the first complete kinematic and dynamic modeling framework for the dVRK-Si PSM. We derive a modified DH kinematic model that captures the closed-chain parallelogram mechanism, formulate dynamics via the Euler-Lagrange method, and express inverse dynamics in a linear-in-parameters regressor form. Dynamic parameters are identified from data collected on a periodic excitation trajectory optimized for numerical conditioning and estimated by convex optimization with physical feasibility constraints. Using the identified model, we implement real-time gravity compensation and computed-torque feedforward in the dVRK control stack. Experiments on a physical dVRK-Si show that the gravity compensation reduces steady-state joint errors by 68-84% and decreases end-effector tip drift during static holds from 4.2 mm to 0.7 mm. Computed-torque feedforward further improves transient and position tracking accuracy. For sinusoidal trajectory tracking, computed-torque feedforward reduces position errors by 35% versus gravity-only feedforward and by 40% versus PID-only. The proposed pipeline supports reliable control, high-fidelity simulation, and learning-based automation on the dVRK-Si. |
| title | Towards Dynamic Model Identification and Gravity Compensation for the dVRK-Si Patient Side Manipulator |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.12099 |