Deep Learning for Real-Time Deformation Estimation in Robotic Material Handling Systems
Keywords:
deformable object manipulation; deformation estimation; physics-informed deep learning; tactile sensing; real-time perception; uncertainty calibrationAbstract
Robotic material handling increasingly involves limp, fibrous, granular, or viscoelastic workpieces whose geometry changes under grasp, acceleration, and transport. Real-time deformation estimation is therefore a perception and control problem rather than a purely geometric one: the robot must infer a dense displacement field, its strain consequences, and the reliability of that inference within the latency of a closed-loop controller. Existing model-based simulation and vision-only tracking methods face complementary limitations in accuracy, calibration burden, occlusion robustness, and computational cost. This paper develops a methodological framework for deep learning-based deformation estimation that fuses visual, tactile, force-torque, and kinematic observations with continuum-mechanical priors. The estimator maps a short history of multimodal observations to a displacement field and derived strain field, using spatiotemporal encoding to represent contact-induced and inertial deformation. Physics-constrained loss terms penalize kinematic incompatibility and implausible material response, while ensemble and heteroscedastic variance estimates expose model uncertainty. The paper specifies input-output definitions, architecture, training objectives, calibration logic, latency constraints, and control integration, and it analyzes occlusion, contact slip, material heterogeneity, simulation-to-reality shift, and overconfident extrapolation. Rather than presenting a single architecture as universally superior, the analysis derives conditions under which physical priors improve data efficiency and when they introduce model bias. The resulting framework provides a defensible design basis for deformation-aware perception and establishes evaluation criteria beyond average displacement error, including strain accuracy, uncertainty calibration, and worst-case latency.
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