受生物启发的可塑神经网络实现复杂机器人零样本泛化
Bio-Inspired Plastic Neural Networks for Zero-Shot Out-of-Distribution Generalization in Complex Animal-Inspired Robots
- 引入权重归一化抑制突触可塑性导致的权值发散
- 在18-DOF蜣螂机器人和16-DOF壁虎机器人上实现零样本仿真到现实迁移
- 可有效应对不平地形与形态损伤等未见场景
人工神经网络虽能解决多种机器人任务，但在分布外（OOD）情况下可能灾难性失效。已有研究采用海布学习（Hebbian learning）这种突触可塑性机制，根据局部神经活动动态调整权重，提升策略鲁棒性并适应环境突变。然而，此类网络易出现权值发散，导致系统不稳定。此外，海布网络尚未应用于具有高自由度的真实复杂机器人。本文改进海布网络，引入权重归一化机制防止权值发散，分析海布权重主成分，并在真实18-DOF蜣螂类机器人与16-DOF壁虎类机器人上全面评估其在步态控制中的性能。结果表明，基于海布的可塑网络可实现零样本仿真到现实的适配，在不平地形及形态损伤等未见条件下仍具泛化能力。
原文摘要 · Abstract (English)
Artificial neural networks can be used to solve a variety of robotic tasks. However, they risk failing catastrophically when faced with out-of-distribution (OOD) situations. Several approaches have employed a type of synaptic plasticity known as Hebbian learning that can dynamically adjust weights based on local neural activities. Research has shown that synaptic plasticity can make policies more robust and help them adapt to unforeseen changes in the environment. However, networks augmented with Hebbian learning can lead to weight divergence, resulting in network instability. Furthermore, such Hebbian networks have not yet been applied to solve legged locomotion in complex real robots with many degrees of freedom. In this work, we improve the Hebbian network with a weight normalization mechanism for preventing weight divergence, analyze the principal components of the Hebbian's weights, and perform a thorough evaluation of network performance in locomotion control for real 18-DOF dung beetle-like and 16-DOF gecko-like robots. We find that the Hebbian-based plastic network can execute zero-shot sim-to-real adaptation locomotion and generalize to unseen conditions, such as uneven terrain and morphological damage.
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