Vision-Based Robot Navigation through Combining Unsupervised Learning and Hierarchical Reinforcement Learning

Abstract

Extensive studies have shown that many animals’ capability of forming spatial representations for self-localization, path planning, and navigation relies on the functionalities of place and head-direction (HD) cells in the hippocampus. Although there are numerous hippocampal modeling approaches, only a few span the wide functionalities ranging from processing raw sensory signals to planning and action generation. This paper presents a vision-based navigation system that involves generating place and HD cells through learning from visual images, building topological maps based on learned cell representations and performing navigation using hierarchical reinforcement learning. First, place and HD cells are trained from sequences of visual stimuli in an unsupervised learning fashion. A modified Slow Feature Analysis (SFA) algorithm is proposed to learn different cell types in an intentional way by restricting their learning to separate phases of the spatial exploration. Then, to extract the encoded metric information from these unsupervised learning representations, a self-organized learning algorithm is adopted to learn over the emerged cell activities and to generate topological maps that reveal the topology of the environment and information about a robot’s head direction, respectively. This enables the robot to perform self-localization and orientation detection based on the generated maps. Finally, goal-directed navigation is performed using reinforcement learning in continuous state spaces which are represented by the population activities of place cells. In particular, considering that the topological map provides a natural hierarchical representation of the environment, hierarchical reinforcement learning (HRL) is used to exploit this hierarchy to accelerate learning. The HRL works on different spatial scales, where a high-level policy learns to select subgoals and a low-level policy learns over primitive actions to specialize on the selected subgoals. Experimental results demonstrate that our system is able to navigate a robot to the desired position effectively, and the HRL shows a much better learning performance than the standard RL in solving our navigation tasks.

Indoor Emergency Path Planning Based on the Q-Learning Optimization Algorithm

“…Reinforcement learning is a method of machine learning, its essence is to find an optimal decision through continuous interaction with the environment [30]. The idea of reinforcement learning is as follows: The agent affects the environment by performing actions.…”

Learning-Based End-to-End Path Planning for Lunar Rovers with Safety Constraints

“…In recent years, with the rapid development of artificial intelligence and deep reinforcement learning (DRL) technology, learning-based path planning, obstacle detection, trafficability analysis and other technologies have been widely concerned by researchers [ 13 , 14 , 15 ]. DRL has the advantages of not requiring environmental maps, strong learning capabilities, and high dynamic adaptability.…”

A Self-Adaptive Reinforcement-Exploration Q-Learning Algorithm

“…Reinforcement learning (RL), one of methodologies of machine learning, is used to describe and solve how an intelligent agent learns and optimizes the strategy during the interaction with the environment [1]. To be more specific, the intelligent agent acquires the reinforcement signal (reward feedback) from the environment during the continuous interaction with the environment, and adjusts its own action strategy through the reward feedback, aiming at the maximum gain.…”