突触运动适应：脉冲神经网络中用于自适应机器人控制的三因子学习规则
Synaptic motor adaptation: A three-factor learning rule for adaptive robotic control in spiking neural networks
AI总结：
本文提出 SMA 算法，利用经梯度下降元优化的三因子突触可塑性规则，使四足机器人仅凭机载传感实现实时在线运动适应，性能达到现有先进方法水平。
摘要：
在真实环境中运行的足式机器人必须具备快速适应意外状况的能力，例如地形变化和载荷变化。本文介绍了 Synaptic Motor Adaptation（SMA，突触运动适应）算法，这是一种通过利用源自神经科学的突触可塑性规则与三因子学习，在四足机器人中实现实时在线适应的新方法。为促进快速适应，我们通过梯度下降对三因子学习规则进行元优化，使其仅使用本地可获取的机载传感数据来近似由特权信息产生的嵌入，从而适应不确定性。我们的算法性能与最先进的运动适应算法相近，并为利用神经形态硬件实现自适应机器人提供了一条清晰路径。
英文摘要：
Legged robots operating in real-world environments must possess the ability to rapidly adapt to unexpected conditions, such as changing terrains and varying payloads. This paper introduces the Synaptic Motor Adaptation (SMA) algorithm, a novel approach to achieving real-time online adaptation in quadruped robots through the utilization of neuroscience-derived rules of synaptic plasticity with three-factor learning. To facilitate rapid adaptation, we meta-optimize a three-factor learning rule via gradient descent to adapt to uncertainty by approximating an embedding produced by privileged information using only locally accessible onboard sensing data. Our algorithm performs similarly to state-of-the-art motor adaptation algorithms and presents a clear path toward achieving adaptive robotics with neuromorphic hardware.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
