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Control techniques like MPC can realize contact-rich manipulation whichexploits dynamic information, maintaining friction limits and safetyconstraints. However, contact geometry and dynamics are required to be known.This information is often extracted from CAD, limiting scalability and theability to handle tasks with varying geometry. To reduce the need for a priorimodels, we propose a framework for estimating contact models online based ontorque and position measurements. To do this, compliant contact models areused, connected in parallel to model multi-point contact and constraints suchas a hinge. They are parameterized to be differentiable with respect to all oftheir parameters (rest position, stiffness, contact location), allowing thecoupled robot/environment dynamics to be linearized or efficiently used ingradient-based optimization. These models are then applied for: offlinegradient-based parameter fitting, online estimation via an extended Kalmanfilter, and online gradient-based MPC. The proposed approach is validated ontwo robots, showing the efficacy of sensorless contact estimation and theeffects of online estimation on MPC performance.

Differentiable Compliant Contact Primitives for Estimation and Model Predictive Control