Single-View RGB-D Force--Deformation Estimation for Robotic Manipulation of Elasto-Plastic Materials

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Article Summary

Persistent deformation complicates elasto-plastic manipulation by coupling physical interaction with spatially distributed shape changes. We investigate the force--deformation relationship using a standard robot, a single eye-in-hand RGB-D camera, and intrinsic force estimates. A 7-DoF Franka Emika FR3 performs normal indentations while an Intel RealSense L515 captures pre- and post-contact surfaces. Point-cloud processing localizes deformation and estimates displaced volume. Force--volume characterization includes 27 measurements over nine force levels. A compact multilayer perceptron (MLP), trained on 52 force--volume pairs at four levels, achieves a held-out RMSE of 1.857 N and $R^2=0.440$. A separate comparison shows lower error for a quadratic model at 16 N and for the MLP at 19 and 23.5 N. An eight-step experiment at 4 N per interaction demonstrates cumulative deformation localization from the second indentation onward. These results establish a practical measurement basis for deformation-aware manipulation using intrinsic force estimates and single-view geometry.

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