Interaction model estimation-based robotic force-position coordinated optimization for rigid-soft heterogeneous contact tasks

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Inspired by Model Predictive Interaction Control (MPIC), this paper proposes differential models for estimating contact geometric parameters and normal-friction forces and formulates an optimal control problem with multiple constraints to allow robots to perform rigid-soft heterogeneous contact tasks. Within the MPIC, robot dynamics are linearized, and Extended Kalman Filters are used for the online estimation of geometry-aware parameters. Meanwhile, a geometry-aware Hertz contact model is introduced for the online estimation of contact forces. We then implement the force-position coordinate optimization by incorporating the contact parameters and interaction force constraints into a gradient-based optimization MPC. Experimental validations were designed for two contact modes: “single-point contact” and “continuous contact”, involving materials with four different Young’s moduli and tested in human arm “relaxation-contraction” task. Results indicate that our framework ensures consistent geometry-aware parameter estimation and maintains reliable force interaction to guarantee safety. Our method reduces the maximum impact force by 50% and decreases the average force error by 42%. The proposed framework has potential applications in medical and industrial tasks involving the manipulation of rigid, soft, and deformable objects.