PM-FSM: Policies Modulating Finite State Machine for Robust Quadrupedal Locomotion. (arXiv:2109.12696v2 [cs.RO] UPDATED)

Deep reinforcement learning (deep RL) has emerged as an effective tool for
developing controllers for legged robots. However, vanilla deep RL often
requires a tremendous amount of training samples and is not feasible for
achieving robust behaviors. Instead, researchers have investigated a novel
policy architecture by incorporating human experts' knowledge, such as Policies
Modulating Trajectory Generators (PMTG). This architecture builds a recurrent
control loop by combining a parametric trajectory generator (TG) and a feedback
policy network to achieve more robust …

arxiv finite state machine fsm machine state