论文标题
学习符号操作员：电动汽车自动拆卸的神经符号解决方案
Learning Symbolic Operators: A Neurosymbolic Solution for Autonomous Disassembly of Electric Vehicle Battery
论文作者
论文摘要
电动汽车的蓬勃发展需要有效的电池拆卸，以使回收环境友好。目前，由于非结构化的环境和高度不确定性，电池拆卸仍然主要由人类完成，可能是机器人的帮助。设计自动解决方案以提高工作效率并降低人类风险在高压和有毒环境中是非常需要的。本文提出了一种新型的神经肯定方法，该方法增强了传统的变分自动编码器（VAE）模型，以根据原始感觉输入及其关系来学习符号操作员。符号运营商包括一个概率状态符号接地模型和一个状态过渡矩阵，用于预测每个执行后的状态，以实现自主任务和运动计划。最后，通过测试结果验证了该方法的可行性。
The booming of electric vehicles demands efficient battery disassembly for recycling to be environment-friendly. Currently, battery disassembly is still primarily done by humans, probably assisted by robots, due to the unstructured environment and high uncertainties. It is highly desirable to design autonomous solutions to improve work efficiency and lower human risks in high voltage and toxic environments. This paper proposes a novel neurosymbolic method, which augments the traditional Variational Autoencoder (VAE) model to learn symbolic operators based on raw sensory inputs and their relationships. The symbolic operators include a probabilistic state symbol grounding model and a state transition matrix for predicting states after each execution to enable autonomous task and motion planning. At last, the method's feasibility is verified through test results.
