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Sample-based learning model predictive control (LMPC) strategies haverecently attracted attention due to their desirable theoretical properties andtheir good empirical performance on robotic tasks. However, prior analysis ofLMPC controllers for stochastic systems has mainly focused on linear systems inthe iterative learning control setting. We present a novel LMPC algorithm,Adjustable Boundary Condition LMPC (ABC-LMPC), which enables rapid adaptationto novel start and goal configurations and theoretically show that theresulting controller guarantees iterative improvement in expectation forstochastic nonlinear systems. We present results with a practical instantiationof this algorithm and experimentally demonstrate that the resulting controlleradapts to a variety of initial and terminal conditions on 3 stochasticcontinuous control tasks.

ABC-LMPC: Safe Sample-Based Learning MPC for Stochastic Nonlinear Dynamical Systems with Adjustable Boundary Conditions