用脉冲神经网络实现太空机械臂多模态自主操作
Multimodal Spiking Neural Network for Space Robotic Manipulation
- 融合几何、触觉与语义信息的脉冲神经网络控制框架
- 任务成功率与能效均优于基线方法
- 适合资源受限的航天器自主作业场景
本文提出一种基于脉冲神经网络（SNN）的多模态控制框架，用于空间站机械臂的自主操作。该框架在有限计算资源下，结合几何状态、触觉与语义信息，增强环境感知能力，提升控制鲁棒性。通过引入双通道三阶段课程强化学习（CRL）机制，逐步引导学习过程。在目标接近、物体抓取及稳定抬升等任务中，使用壁挂式机械臂进行测试，表现稳定可靠。实验表明，该方法在任务成功率和能量效率方面持续优于基线方法，具备实际航空航天应用潜力。
原文摘要 · Abstract (English)
This paper presents a multimodal control framework based on spiking neural networks (SNNs) for robotic arms aboard space stations. It is designed to cope with the constraints of limited onboard resources while enabling autonomous manipulation and material transfer in space operations. By combining geometric states with tactile and semantic information, the framework strengthens environmental awareness and contributes to more robust control strategies. To guide the learning process progressively, a dual-channel, three-stage curriculum reinforcement learning (CRL) scheme is further integrated into the system. The framework was tested across a range of tasks including target approach, object grasping, and stable lifting with wall-mounted robotic arms, demonstrating reliable performance throughout. Experimental evaluations demonstrate that the proposed method consistently outperforms baseline approaches in both task success rate and energy efficiency. These findings highlight its suitability for real-world aerospace applications.
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