Modern computational models have leveraged biological advances in human brain research. This study addresses the problem of multimodal learning with the help of brain-inspired models. Specifically, a unified multimodal learning architecture is proposed based on deep neural networks, which are inspired by the biology of the visual cortex of the human brain. This unified framework is validated by two practical multimodal learning tasks: image captioning, involving visual and natural language signals, and visual-haptic fusion, involving haptic and visual signals. Extensive experiments are conducted under the framework, and competitive results are achieved.
机械臂运动规划是机器人研究领域的重点,对机械臂能否顺利执行任务非常重要。目前,机械臂运动规划多使用RRT法,然而该方法是在关节空间进行规划,无法适用于机械臂末端执行器存在约束的任务。为了克服这个不足,该文提出了一种任务自由子空间RRT(rapidly-exploring random tree)法,在末端执行器任务空间的自由子空间中构建RRT,并对其每步扩张进行逆运动学轨迹优化,求解出相应的关节轨迹。此外,由于末端执行器速度对逆运动规划有重要影响,该文在逆运动轨迹优化阶段采用了最似梯度法,不仅考虑了末端执行器的运动速度,而且通过极小关节自由速度和优化目标负梯度的距离,重新确定了关节自由速度,增强了算法的优化能力。实验结果表明:该算法能有效解决机械臂末端存在约束的问题。
