F-CIP recasts the Controllable Information Production objective as an RL-native formulation, discovering balancing and controllability without selecting information variables and, with a simple forward-velocity reward, producing hopping and running gaits
The authors introduce Forward CIP (F-CIP), an RL-native formulation of the Controllable Information Production (CIP) objective that is defined by the system's dynamics alone and requires no selection of information variables; they prove F-CIP is compatible with RL and demonstrate its effectiveness with existing algorithms, where training yields unsupervised discovery of primitive behaviors such as balancing and maintaining controllability, and pairing it with a simple forward-velocity reward produces coordinated gaits such as hopping and running that otherwise require reward engineering.