RigPI: Dynamic Parameter Identification of Rigid Body via VLM-Seeded Differentiable Simulation

• URL: http://arxiv.org/abs/2606.25212v2
• Date: Thu, 25 Jun 2026 06:30:32 GMT
• Title: RigPI: Dynamic Parameter Identification of Rigid Body via VLM-Seeded Differentiable Simulation
• Abstract summary: RigPI is a framework for identifying dynamic parameters of rigid bodies and multi-link rigid bodies during robot-object interaction.<n> RigPI integrates vision-based semantic priors, force-torque measurements, and motion observations within a differentiable simulation pipeline.<n>Extensive real-world experiments on objects with revolute and prismatic joints demonstrate that RigPI achieves accurate and stable parameter estimates.
• Score: 16.686983429272214
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate physical parameter identification of manipulated objects is fundamental to advanced robotic manipulation and the construction of faithful digital twins. However, acquiring physically consistent inertial and frictional properties from real-world interactions remains challenging due to sensing noise, modeling errors, and limited prior knowledge. This paper presents RigPI, a systematic framework for identifying dynamic parameters of both unconstrained rigid bodies and multi-link rigid bodies during robot-object interaction. RigPI integrates vision-based semantic priors, force-torque measurements, and motion observations within a differentiable simulation pipeline. A vision-language model (VLM) provides informed initialization and a constrained search space, while gradient information from a differentiable physics simulator enables efficient and stable parameter refinement. The proposed two-stage optimization strategy alleviates sensitivity to noise and avoids physically implausible solutions. Extensive real-world experiments on objects with revolute and prismatic joints demonstrate that RigPI achieves accurate and stable parameter estimates, and successfully reproduces manipulation trajectories on a real robot with parameter-aware predictive validity. These results highlight the effectiveness and robustness of RigPI for real-world robotic system identification tasks.

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