Abstract: A system is controlled by an actor-critic based fuzzy reinforcement learning algorithm that provides instructions to a processor of the system for applying actor-critic based fuzzy reinforcement learning. The system includes a database of fuzzy-logic rules for mapping input data to output commands for modifying a system state, and a reinforcement learning algorithm for updating the fuzzy-logic rules database based on effects on the system state of the output commands mapped from the input data. The reinforcement learning algorithm is configured to converge at least one parameter of the system state to at least approximately an optimum value following multiple mapping and updating iterations. The reinforcement learning algorithm may be based on an update equation including a derivative with respect to at least one parameter of a logarithm of a probability function for taking a selected action when a selected state is encountered.

Abstract: A biologically inspired, multi-segmented robot is provided, wherein the biologically inspired, multi-segmented robot employs a logic-based technique to control mobility, rather than a mobility control technique that relies on complex mathematical models. The robot is biologically-inspired in that it employs certain artificial, anatomical and neuro-physiological features that are similar to features found in actual biological systems, including a host of artificial joints and muscle-like actuators. These anatomical and neuro-physiological features exhibit certain built-in, mechanical constraints, which provide mechanical feedback that is similar to the type of feedback that is inherent in genuine biological systems. In addition, the robot employs one or more sensors which are capable of measuring the status (e.g., the position) of the robot. A controller then uses the sensor data to activate functional groupings of the muscle-like activators to control mobility.