Discrete fully probabilistic design: towards a control pipeline for the synthesis of policies from examples
This work addresses a control synthesis problem for robotics or autonomous systems, offering a novel approach that is incremental in building upon prior algorithms.
The paper tackles the problem of synthesizing control policies from example data for constrained, stochastic, and nonlinear systems, achieving this by introducing a discrete fully probabilistic design pipeline that works even with noisy or mismatched example data, as demonstrated numerically on an inverted pendulum example.
We present the principled design of a control pipeline for the synthesis of policies from examples data. The pipeline, based on a discretized design which we term as discrete fully probabilistic design, expounds an algorithm recently introduced in Gagliardi and Russo (2021) to synthesize policies from examples for constrained, stochastic and nonlinear systems. Contrary to other approaches, the pipeline we present: (i) does not need the constraints to be fulfilled in the possibly noisy example data; (ii) enables control synthesis even when the data are collected from an example system that is different from the one under control. The design is benchmarked numerically on an example that involves controlling an inverted pendulum with actuation constraints starting from data collected from a physically different pendulum that does not satisfy the system-specific actuation constraints. We also make our fully documented code openly available.