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Planning over discontinuous dynamics is needed for robotics tasks likecontact-rich manipulation, which presents challenges in the numerical stabilityand speed of planning methods when either neural network or analytical modelsare used. On the one hand, sampling-based planners require higher samplecomplexity in high-dimensional problems and cannot describe safety constraintssuch as force limits. On the other hand, gradient-based solvers can suffer fromlocal optima and convergence issues when the Hessian is poorly conditioned. Wepropose a planning method with both sampling- and gradient-based elements,using the Cross-entropy Method to initialize a gradient-based solver, providingbetter search over local minima and the ability to handle explicit constraints.We show the approach allows smooth, stable contact-rich planning for animpedance-controlled robot making contact with a stiff environment,benchmarking against gradient-only MPC and CEM.

Combining Sampling- and Gradient-based Planning for Contact-rich Manipulation