Enhancing Binocular Depth Estimation Based on Proactive Perception and Action Cyclic Learning for an Autonomous Developmental Robot

In humans, perception and action (PA) possess cyclically causal relations. In this paper, we propose a new PA-based cyclic learning framework to autonomously enhance the depth-estimation accuracy of a humanoid robot and perform given behavioral tasks. The proposed method integrates the concepts of sensory invariance-driven action and object-size invariance to autonomously enhance the depth-estimation accuracy. If the depth estimation is reliable, the reinforcement learning framework is used to generate goal-directed actions of a humanoid robot based on a perceived environment. Iterative PA cycles of a robot autonomously refine its depth-estimation. The proposed method is evaluated using a humanoid robot (NAO) with stereo cameras, and the experimental results demonstrate that the proposed framework is effective for autonomously enhancing both the depth-estimation accuracy and the action-generation performance.

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