Model-based learning for mobile robot navigation from the dynamical systems perspective

TLDR

The study aims to solve the symbol grounding problem and situate internal symbolic processes within behavioral contexts by applying a dynamical systems approach to robot navigation learning. It constructs a symbolic process through forward modeling with recurrent neural networks that map local laser‑range‑finder sensory inputs to environmental dynamics, enabling the robot to learn and plan navigation. Experiments show the robot learns grammatical structure from workspace geometry, generates diverse goal‑directed action plans using a chaotic forward model, and demonstrates self‑organized, structurally stable symbolic processes grounded in physical interaction.

Abstract

This paper discusses how a behavior-based robot can construct a "symbolic process" that accounts for its deliberative thinking processes using models of the environment. The paper focuses on two essential problems; one is the symbol grounding problem and the other is how the internal symbolic processes can be situated with respect to the behavioral contexts. We investigate these problems by applying a dynamical system's approach to the robot navigation learning problem. Our formulation, based on a forward modeling scheme using recurrent neural learning, shows that the robot is capable of learning grammatical structure hidden in the geometry of the workspace from the local sensory inputs through its navigational experiences. Furthermore, the robot is capable of generating diverse action plans to reach an arbitrary goal using the acquired forward model which incorporates chaotic dynamics. The essential claim is that the internal symbolic process, being embedded in the attractor, is grounded since it is self-organized solely through interaction with the physical world. It is also shown that structural stability arises in the interaction between the neural dynamics and the environmental dynamics, which accounts for the situatedness of the internal symbolic process, The experimental results using a mobile robot, equipped with a local sensor consisting of a laser range finder, verify our claims.