Robotics: Science and Systems XVII
Entropy-Guided Control Improvisation
Marcell J Vazquez-Chanlatte, Sebastian Junges, Daniel J Fremont, Sanjit SeshiaAbstract:
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.
Bibtex:
@INPROCEEDINGS{Vazquez-Chanlatte-RSS-21, 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} }