Episodic Memory-Based Robotic Planning Under Uncertainty

This paper presents a robotic behavior planning method under uncertainty based on biology-inspired episodic memory. Adaptive behavior planning, prediction and reasoning are achieved between tasks, environment, and threats. Through building a novel episode model and introducing the activation and stimulation mechanism of state neurons, the framework of an episodic memory-driving Markov decision process (EM-MDP) is proposed for incremental self-learning of robotic experience and cognitive behavior planning. Two main challenges in robot behavior control under uncertainty are addressed: high computational complexity and perceptual aliasing. The approach for robotic global planning and behaviors sequence prediction based on the EM-MDP is developed utilizing neuron synaptic potential. A local behavioral planning method based on risk function and feasible paths is employed to achieve path optimization and behavior reasoning under the condition of imperfect memory. Robot can evaluate the past events sequence, predict the current state, and plan the desired behaviors. The proposed method is evaluated in several real-life environments for a mobile robot system. The robot system is able to successfully produce solutions in general scenarios under uncertainty.