将仿真训练的神经网络转为脉冲神经网络，实现在英特尔洛伊希2芯片上的低功耗实时机器人控制。
Autonomous Reinforcement Learning Robot Control with Intel's Loihi 2 Neuromorphic Hardware
- 通过转换器将传统神经网络变为适合洛伊希2的脉冲神经网络
- 在洛伊希2上实现低延迟、低功耗的机器人闭环控制，性能优于GPU
- 为未来太空与地面机器人的能效型实时计算提供可行路径
我们提出一个端到端流程，将强化学习（RL）训练的类人神经网络（ANN）转化为适用于英特尔洛伊希2架构的脉冲Sigma-Delta神经网络（SDNN），实现低延迟、低功耗推理。以自由飞行的Astrobee机器人控制为测试案例，该策略在仿真中完全训练，使用修正线性单元（ReLUs）构建，经转换后部署于洛伊希2芯片，并在NVIDIA Omniverse Isaac Lab环境中评估其闭环运动控制表现。与GPU对比，洛伊希2在执行效率和能耗上均表现更优。结果验证了神经形态平台用于机器人控制的可行性，为未来空间及地面机器人应用中的能效型实时神经形态计算提供了可拓展路径。
原文摘要 · Abstract (English)
We present an end-to-end pipeline for deploying reinforcement learning (RL) trained Artificial Neural Networks (ANNs) on neuromorphic hardware by converting them into spiking Sigma-Delta Neural Networks (SDNNs). We demonstrate that an ANN policy trained entirely in simulation can be transformed into an SDNN compatible with Intel's Loihi 2 architecture, enabling low-latency and energy-efficient inference. As a test case, we use an RL policy for controlling the Astrobee free-flying robot, similar to a previously hardware in space-validated controller. The policy, trained with Rectified Linear Units (ReLUs), is converted to an SDNN and deployed on Intel's Loihi 2, then evaluated in NVIDIA's Omniverse Isaac Lab simulation environment for closed-loop control of Astrobee's motion. We compare execution performance between GPU and Loihi 2. The results highlight the feasibility of using neuromorphic platforms for robotic control and establish a pathway toward energy-efficient, real-time neuromorphic computation in future space and terrestrial robotics applications.
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