Robotics: Science and Systems XVII

Entropy-Guided Control Improvisation

Marcell J Vazquez-Chanlatte, Sebastian Junges, Daniel J Fremont, Sanjit Seshia


High level declarative constraints provide a powerful (and popular) way to define and construct control policies; however; most synthesis algorithms do not support specifying the degree of randomness (unpredictability) of the resulting controller. In many contexts; e.g.; patrolling; testing; behavior prediction; and planning on idealized models; predictable or biased controllers are undesirable. To address these concerns; we introduce the Entropic Reactive Control Improvisation (ERCI) framework and algorithm that supports synthesizing control policies for stochastic games that are declaratively specified by (i) a hard constraint specifying what must occur (ii) a soft constraint specifying what typically occurs; and (iii) a randomization constraint specifying the unpredictability and variety of the controller; as quantified using causal entropy. This framework; which extends the state-of-the-art by supporting arbitrary combinations of adversarial and probabilistic uncertainty in the environment; enables a flexible modeling formalism which we argue; theoretically and empirically; remains tractable.



    AUTHOR    = {Marcell J Vazquez-Chanlatte AND Sebastian Junges AND Daniel J Fremont AND Sanjit Seshia}, 
    TITLE     = {{Entropy-Guided Control Improvisation}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2021}, 
    ADDRESS   = {Virtual}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2021.XVII.051}