Enhancing Binocular Depth Estimation Based on Proactive Perception and Action Cyclic Learning for an Autonomous Developmental Robot
提出一种感知与动作循环学习框架，让仿人机器人通过自主迭代提升双目深度估计精度，并完成指定行为任务，实验验证了有效性。
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.